{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOJ3W2rnkrTT",
        "outputId": "d05d4c8a-23db-413c-a269-62e85f7c69b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-11-24 14:31:12--  http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat [following]\n",
            "--2024-11-24 14:31:13--  https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5953527 (5.7M)\n",
            "Saving to: ‘Indian_pines_corrected.mat’\n",
            "\n",
            "Indian_pines_correc 100%[===================>]   5.68M  1.77MB/s    in 3.2s    \n",
            "\n",
            "2024-11-24 14:31:17 (1.77 MB/s) - ‘Indian_pines_corrected.mat’ saved [5953527/5953527]\n",
            "\n",
            "URL transformed to HTTPS due to an HSTS policy\n",
            "--2024-11-24 14:31:17--  https://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1125 (1.1K)\n",
            "Saving to: ‘Indian_pines_gt.mat’\n",
            "\n",
            "Indian_pines_gt.mat 100%[===================>]   1.10K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-11-24 14:31:18 (71.9 MB/s) - ‘Indian_pines_gt.mat’ saved [1125/1125]\n",
            "\n",
            "Collecting spectral\n",
            "  Downloading spectral-0.23.1-py3-none-any.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from spectral) (1.26.4)\n",
            "Downloading spectral-0.23.1-py3-none-any.whl (212 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.9/212.9 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ],
      "source": [
        "! wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
        "! wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
        "! pip install spectral"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io as sio\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "import spectral\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "DEJppQvhkyYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_num = 16\n",
        "\n",
        "class HybridSN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(HybridSN, self).__init__()\n",
        "        self.conv3d1 = nn.Conv3d(in_channels=1, out_channels=8, kernel_size=(7, 3, 3))\n",
        "        self.conv3d2 = nn.Conv3d(in_channels=8, out_channels=16, kernel_size=(5, 3, 3))\n",
        "        self.conv3d3 = nn.Conv3d(in_channels=16, out_channels=32, kernel_size=(3, 3, 3))\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.fc1 = nn.Linear(32 * 18 * 19 * 19, 256)\n",
        "        self.fc2 = nn.Linear(256, 128)\n",
        "        self.fc3 = nn.Linear(128, class_num)\n",
        "        self.dropout = nn.Dropout(p=0.4)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv3d1(x))\n",
        "        x = F.relu(self.conv3d2(x))\n",
        "        x = F.relu(self.conv3d3(x))\n",
        "        x = x.view(-1, 32 * 18 * 19 * 19)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc3(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "_9ObRIK-k4vz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 对高光谱数据 X 应用 PCA 变换\n",
        "def applyPCA(X, numComponents):\n",
        "    newX = np.reshape(X, (-1, X.shape[2]))\n",
        "    pca = PCA(n_components=numComponents, whiten=True)\n",
        "    newX = pca.fit_transform(newX)\n",
        "    newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents))\n",
        "    return newX\n",
        "\n",
        "# 对单个像素周围提取 patch 时，边缘像素就无法取了，因此，给这部分像素进行 padding 操作\n",
        "def padWithZeros(X, margin=2):\n",
        "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n",
        "    return newX\n",
        "\n",
        "# 在每个像素周围提取 patch ，然后创建成符合 keras 处理的格式\n",
        "def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    # 给 X 做 padding\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = padWithZeros(X, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))\n",
        "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]\n",
        "            patchesData[patchIndex, :, :, :] = patch\n",
        "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    if removeZeroLabels:\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\n",
        "        patchesLabels -= 1\n",
        "    return patchesData, patchesLabels\n",
        "\n",
        "def splitTrainTestSet(X, y, testRatio, randomState=345):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "# 地物类别\n",
        "class_num = 16\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "# 用于测试样本的比例\n",
        "test_ratio = 0.90\n",
        "# 每个像素周围提取 patch 的尺寸\n",
        "patch_size = 25\n",
        "# 使用 PCA 降维，得到主成分的数量\n",
        "pca_components = 30\n",
        "\n",
        "print('Hyperspectral data shape: ', X.shape)\n",
        "print('Label shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... PCA tranformation ... ...')\n",
        "X_pca = applyPCA(X, numComponents=pca_components)\n",
        "print('Data shape after PCA: ', X_pca.shape)\n",
        "\n",
        "print('\\n... ... create data cubes ... ...')\n",
        "X_pca, y = createImageCubes(X_pca, y, windowSize=patch_size)\n",
        "print('Data cube X shape: ', X_pca.shape)\n",
        "print('Data cube y shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... create train & test data ... ...')\n",
        "Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X_pca, y, test_ratio)\n",
        "print('Xtrain shape: ', Xtrain.shape)\n",
        "print('Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 改变 Xtrain, Ytrain 的形状，以符合 keras 的要求\n",
        "Xtrain = Xtrain.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "Xtest  = Xtest.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "print('before transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('before transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 为了适应 pytorch 结构，数据要做 transpose\n",
        "Xtrain = Xtrain.transpose(0, 4, 3, 1, 2)\n",
        "Xtest  = Xtest.transpose(0, 4, 3, 1, 2)\n",
        "print('after transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('after transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "\n",
        "\"\"\" Training dataset\"\"\"\n",
        "class TrainDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtrain.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtrain)\n",
        "        self.y_data = torch.LongTensor(ytrain)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "\"\"\" Testing dataset\"\"\"\n",
        "class TestDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtest.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtest)\n",
        "        self.y_data = torch.LongTensor(ytest)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "# 创建 trainloader 和 testloader\n",
        "trainset = TrainDS()\n",
        "testset  = TestDS()\n",
        "train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)\n",
        "test_loader  = torch.utils.data.DataLoader(dataset=testset,  batch_size=128, shuffle=False, num_workers=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I01LQysmk9Lp",
        "outputId": "61f61722-339c-4207-f049-aed3c293e54d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperspectral data shape:  (145, 145, 200)\n",
            "Label shape:  (145, 145)\n",
            "\n",
            "... ... PCA tranformation ... ...\n",
            "Data shape after PCA:  (145, 145, 30)\n",
            "\n",
            "... ... create data cubes ... ...\n",
            "Data cube X shape:  (10249, 25, 25, 30)\n",
            "Data cube y shape:  (10249,)\n",
            "\n",
            "... ... create train & test data ... ...\n",
            "Xtrain shape:  (1024, 25, 25, 30)\n",
            "Xtest  shape:  (9225, 25, 25, 30)\n",
            "before transpose: Xtrain shape:  (1024, 25, 25, 30, 1)\n",
            "before transpose: Xtest  shape:  (9225, 25, 25, 30, 1)\n",
            "after transpose: Xtrain shape:  (1024, 1, 30, 25, 25)\n",
            "after transpose: Xtest  shape:  (9225, 1, 30, 25, 25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 使用GPU训练，可以在菜单 \"代码执行工具\" -> \"更改运行时类型\" 里进行设置\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 网络放到GPU上\n",
        "net = HybridSN().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "b_Bw7cCelA1W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 记录每次epoch的平均训练损失\n",
        "train_losses_avg = []\n",
        "# 记录每次epoch的当前损失\n",
        "train_losses_current = []\n",
        "# 开始训练\n",
        "net.train()\n",
        "total_loss = 0\n",
        "for epoch in range(100):\n",
        "    current_loss = 0\n",
        "    for i, (inputs, labels) in enumerate(train_loader):\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # 优化器梯度归零\n",
        "        optimizer.zero_grad()\n",
        "        # 正向传播 +　反向传播 + 优化\n",
        "        outputs = net(inputs)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        current_loss += loss.item()\n",
        "    train_losses_avg.append(total_loss / (epoch+1))\n",
        "    train_losses_current.append(current_loss)\n",
        "    print('[Epoch: %d]   [loss avg: %.4f]   [current loss: %.4f]' %(epoch + 1, total_loss/(epoch+1), current_loss))\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# 可视化训练损失\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses_avg, label='Average Loss per Epoch')\n",
        "plt.plot(train_losses_current, label='Current Loss per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss per Epoch')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'training_loss_NoSE_NoSoftmax_NoDropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()\n",
        "\n",
        "# 保存训练后的模型\n",
        "torch.save(net.state_dict(), 'HybirdSN_model.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_qNhaNNdlEDU",
        "outputId": "51ddb8f0-5378-474f-e56a-7d1d902ad90e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch: 1]   [loss avg: 21.1056]   [current loss: 21.1056]\n",
            "[Epoch: 2]   [loss avg: 20.1775]   [current loss: 19.2495]\n",
            "[Epoch: 3]   [loss avg: 19.2622]   [current loss: 17.4317]\n",
            "[Epoch: 4]   [loss avg: 17.8625]   [current loss: 13.6632]\n",
            "[Epoch: 5]   [loss avg: 16.0305]   [current loss: 8.7028]\n",
            "[Epoch: 6]   [loss avg: 14.1358]   [current loss: 4.6622]\n",
            "[Epoch: 7]   [loss avg: 12.5735]   [current loss: 3.1995]\n",
            "[Epoch: 8]   [loss avg: 11.2201]   [current loss: 1.7463]\n",
            "[Epoch: 9]   [loss avg: 10.1107]   [current loss: 1.2353]\n",
            "[Epoch: 10]   [loss avg: 9.1944]   [current loss: 0.9479]\n",
            "[Epoch: 11]   [loss avg: 8.4231]   [current loss: 0.7104]\n",
            "[Epoch: 12]   [loss avg: 7.7711]   [current loss: 0.5988]\n",
            "[Epoch: 13]   [loss avg: 7.2054]   [current loss: 0.4166]\n",
            "[Epoch: 14]   [loss avg: 6.7334]   [current loss: 0.5976]\n",
            "[Epoch: 15]   [loss avg: 6.3207]   [current loss: 0.5428]\n",
            "[Epoch: 16]   [loss avg: 5.9532]   [current loss: 0.4411]\n",
            "[Epoch: 17]   [loss avg: 5.6277]   [current loss: 0.4193]\n",
            "[Epoch: 18]   [loss avg: 5.3343]   [current loss: 0.3470]\n",
            "[Epoch: 19]   [loss avg: 5.0650]   [current loss: 0.2174]\n",
            "[Epoch: 20]   [loss avg: 4.8221]   [current loss: 0.2078]\n",
            "[Epoch: 21]   [loss avg: 4.6055]   [current loss: 0.2736]\n",
            "[Epoch: 22]   [loss avg: 4.4103]   [current loss: 0.3093]\n",
            "[Epoch: 23]   [loss avg: 4.2264]   [current loss: 0.1822]\n",
            "[Epoch: 24]   [loss avg: 4.0564]   [current loss: 0.1465]\n",
            "[Epoch: 25]   [loss avg: 3.8993]   [current loss: 0.1283]\n",
            "[Epoch: 26]   [loss avg: 3.7566]   [current loss: 0.1899]\n",
            "[Epoch: 27]   [loss avg: 3.6261]   [current loss: 0.2322]\n",
            "[Epoch: 28]   [loss avg: 3.5010]   [current loss: 0.1229]\n",
            "[Epoch: 29]   [loss avg: 3.3856]   [current loss: 0.1547]\n",
            "[Epoch: 30]   [loss avg: 3.2752]   [current loss: 0.0722]\n",
            "[Epoch: 31]   [loss avg: 3.1742]   [current loss: 0.1453]\n",
            "[Epoch: 32]   [loss avg: 3.0770]   [current loss: 0.0655]\n",
            "[Epoch: 33]   [loss avg: 2.9863]   [current loss: 0.0820]\n",
            "[Epoch: 34]   [loss avg: 2.9021]   [current loss: 0.1256]\n",
            "[Epoch: 35]   [loss avg: 2.8214]   [current loss: 0.0744]\n",
            "[Epoch: 36]   [loss avg: 2.7467]   [current loss: 0.1339]\n",
            "[Epoch: 37]   [loss avg: 2.6760]   [current loss: 0.1326]\n",
            "[Epoch: 38]   [loss avg: 2.6089]   [current loss: 0.1229]\n",
            "[Epoch: 39]   [loss avg: 2.5441]   [current loss: 0.0822]\n",
            "[Epoch: 40]   [loss avg: 2.4822]   [current loss: 0.0682]\n",
            "[Epoch: 41]   [loss avg: 2.4270]   [current loss: 0.2210]\n",
            "[Epoch: 42]   [loss avg: 2.3725]   [current loss: 0.1349]\n",
            "[Epoch: 43]   [loss avg: 2.3197]   [current loss: 0.1042]\n",
            "[Epoch: 44]   [loss avg: 2.2705]   [current loss: 0.1558]\n",
            "[Epoch: 45]   [loss avg: 2.2228]   [current loss: 0.1211]\n",
            "[Epoch: 46]   [loss avg: 2.1761]   [current loss: 0.0780]\n",
            "[Epoch: 47]   [loss avg: 2.1322]   [current loss: 0.1131]\n",
            "[Epoch: 48]   [loss avg: 2.0894]   [current loss: 0.0744]\n",
            "[Epoch: 49]   [loss avg: 2.0477]   [current loss: 0.0487]\n",
            "[Epoch: 50]   [loss avg: 2.0092]   [current loss: 0.1222]\n",
            "[Epoch: 51]   [loss avg: 1.9736]   [current loss: 0.1933]\n",
            "[Epoch: 52]   [loss avg: 1.9369]   [current loss: 0.0668]\n",
            "[Epoch: 53]   [loss avg: 1.9014]   [current loss: 0.0546]\n",
            "[Epoch: 54]   [loss avg: 1.8667]   [current loss: 0.0245]\n",
            "[Epoch: 55]   [loss avg: 1.8348]   [current loss: 0.1153]\n",
            "[Epoch: 56]   [loss avg: 1.8029]   [current loss: 0.0487]\n",
            "[Epoch: 57]   [loss avg: 1.7725]   [current loss: 0.0675]\n",
            "[Epoch: 58]   [loss avg: 1.7432]   [current loss: 0.0754]\n",
            "[Epoch: 59]   [loss avg: 1.7149]   [current loss: 0.0718]\n",
            "[Epoch: 60]   [loss avg: 1.6869]   [current loss: 0.0370]\n",
            "[Epoch: 61]   [loss avg: 1.6611]   [current loss: 0.1137]\n",
            "[Epoch: 62]   [loss avg: 1.6361]   [current loss: 0.1101]\n",
            "[Epoch: 63]   [loss avg: 1.6131]   [current loss: 0.1872]\n",
            "[Epoch: 64]   [loss avg: 1.5882]   [current loss: 0.0163]\n",
            "[Epoch: 65]   [loss avg: 1.5663]   [current loss: 0.1676]\n",
            "[Epoch: 66]   [loss avg: 1.5442]   [current loss: 0.1099]\n",
            "[Epoch: 67]   [loss avg: 1.5223]   [current loss: 0.0730]\n",
            "[Epoch: 68]   [loss avg: 1.5009]   [current loss: 0.0664]\n",
            "[Epoch: 69]   [loss avg: 1.4803]   [current loss: 0.0832]\n",
            "[Epoch: 70]   [loss avg: 1.4603]   [current loss: 0.0799]\n",
            "[Epoch: 71]   [loss avg: 1.4402]   [current loss: 0.0320]\n",
            "[Epoch: 72]   [loss avg: 1.4218]   [current loss: 0.1161]\n",
            "[Epoch: 73]   [loss avg: 1.4035]   [current loss: 0.0870]\n",
            "[Epoch: 74]   [loss avg: 1.3861]   [current loss: 0.1120]\n",
            "[Epoch: 75]   [loss avg: 1.3683]   [current loss: 0.0521]\n",
            "[Epoch: 76]   [loss avg: 1.3506]   [current loss: 0.0237]\n",
            "[Epoch: 77]   [loss avg: 1.3343]   [current loss: 0.0946]\n",
            "[Epoch: 78]   [loss avg: 1.3178]   [current loss: 0.0452]\n",
            "[Epoch: 79]   [loss avg: 1.3022]   [current loss: 0.0884]\n",
            "[Epoch: 80]   [loss avg: 1.2871]   [current loss: 0.0946]\n",
            "[Epoch: 81]   [loss avg: 1.2716]   [current loss: 0.0311]\n",
            "[Epoch: 82]   [loss avg: 1.2568]   [current loss: 0.0589]\n",
            "[Epoch: 83]   [loss avg: 1.2430]   [current loss: 0.1136]\n",
            "[Epoch: 84]   [loss avg: 1.2293]   [current loss: 0.0885]\n",
            "[Epoch: 85]   [loss avg: 1.2156]   [current loss: 0.0645]\n",
            "[Epoch: 86]   [loss avg: 1.2026]   [current loss: 0.0982]\n",
            "[Epoch: 87]   [loss avg: 1.1906]   [current loss: 0.1572]\n",
            "[Epoch: 88]   [loss avg: 1.1784]   [current loss: 0.1213]\n",
            "[Epoch: 89]   [loss avg: 1.1658]   [current loss: 0.0522]\n",
            "[Epoch: 90]   [loss avg: 1.1537]   [current loss: 0.0828]\n",
            "[Epoch: 91]   [loss avg: 1.1418]   [current loss: 0.0696]\n",
            "[Epoch: 92]   [loss avg: 1.1307]   [current loss: 0.1136]\n",
            "[Epoch: 93]   [loss avg: 1.1188]   [current loss: 0.0324]\n",
            "[Epoch: 94]   [loss avg: 1.1075]   [current loss: 0.0550]\n",
            "[Epoch: 95]   [loss avg: 1.0967]   [current loss: 0.0761]\n",
            "[Epoch: 96]   [loss avg: 1.0857]   [current loss: 0.0423]\n",
            "[Epoch: 97]   [loss avg: 1.0748]   [current loss: 0.0346]\n",
            "[Epoch: 98]   [loss avg: 1.0641]   [current loss: 0.0238]\n",
            "[Epoch: 99]   [loss avg: 1.0535]   [current loss: 0.0143]\n",
            "[Epoch: 100]   [loss avg: 1.0433]   [current loss: 0.0340]\n",
            "Finished Training\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 控制第一次迭代时是否初始化预测结果数组\n",
        "count = 0\n",
        "# 记录测试集中分类正确的样本数量\n",
        "correct_test = 0\n",
        "# 记录测试集中的总样本数量\n",
        "total_test = 0\n",
        "# 记录每个batch的准确率\n",
        "val_accuracies = []\n",
        "\n",
        "# 在测试集上计算准确率并预测结果\n",
        "for inputs, labels in test_loader:\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = net(inputs)\n",
        "\n",
        "    # 获取预测结果\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "    # 更新测试准确率\n",
        "    correct_test += (predicted == labels).sum().item()\n",
        "    total_test += labels.size(0)\n",
        "\n",
        "    # 将预测结果添加到y_pred_test\n",
        "    outputs = np.argmax(outputs.detach().cpu().numpy(), axis=1)\n",
        "    if count == 0:\n",
        "        y_pred_test = outputs\n",
        "        count = 1\n",
        "    else:\n",
        "        y_pred_test = np.concatenate((y_pred_test, outputs))\n",
        "\n",
        "    # 计算当前batch的准确率\n",
        "    batch_accuracy = (predicted == labels).sum().item() / labels.size(0)\n",
        "    val_accuracies.append(batch_accuracy)\n",
        "\n",
        "# 总准确率\n",
        "test_accuracy = correct_test / total_test\n",
        "\n",
        "# 生成分类报告\n",
        "classification = classification_report(ytest, y_pred_test, digits=4)\n",
        "print(classification)\n",
        "\n",
        "# 可视化准确率\n",
        "plt.plot(val_accuracies, label='Test Accuracy per Batch')\n",
        "plt.axhline(y=test_accuracy, color='r', linestyle='--', label=f'Overall Accuracy: {test_accuracy:.4f}')\n",
        "plt.title('Test Accuracy During Testing')\n",
        "plt.xlabel('Batch Index')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'testing_accuracy_NoSE_NoSoftmax_NoDropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "32N2pdiomCIG",
        "outputId": "2e7a4a97-9dd8-4f6e-d55c-bb5a6b0bddaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.8696    0.9756    0.9195        41\n",
            "         1.0     0.9813    0.9385    0.9594      1285\n",
            "         2.0     0.9916    0.9531    0.9720       747\n",
            "         3.0     0.9587    0.9812    0.9698       213\n",
            "         4.0     0.9729    0.9885    0.9806       435\n",
            "         5.0     0.9862    0.9802    0.9832       657\n",
            "         6.0     0.9615    1.0000    0.9804        25\n",
            "         7.0     0.9795    1.0000    0.9896       430\n",
            "         8.0     1.0000    0.7222    0.8387        18\n",
            "         9.0     0.9731    0.9909    0.9819       875\n",
            "        10.0     0.9646    0.9869    0.9756      2210\n",
            "        11.0     0.9481    0.9588    0.9534       534\n",
            "        12.0     0.9888    0.9514    0.9697       185\n",
            "        13.0     0.9835    0.9965    0.9900      1139\n",
            "        14.0     0.9941    0.9654    0.9795       347\n",
            "        15.0     0.9125    0.8690    0.8902        84\n",
            "\n",
            "    accuracy                         0.9743      9225\n",
            "   macro avg     0.9666    0.9536    0.9584      9225\n",
            "weighted avg     0.9746    0.9743    0.9742      9225\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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bb+Pmm2/G+eefj3HjxuFnP/sZHnnkEWzevFkIR33wwQcYN26c8FnXXXcdfv/73+O6667Dcccdh/fffx/fffed7t+svLwcP/nJT/DAAw8gFAqhX79++Pe//42Ghoaksb/73e/w73//G2PHjsUNN9yAI444Anv27MFLL72ENWvWSDwwkydPxiOPPIJ3330Xf/jDH3TPB4gJr5999llEo1G0tLTg008/FUThS5culRhTZ555JgYMGIBrr70Wv/jFL2C1WvHMM8+gtrY2L60zevXqhdtuuw3z58/HeeedhwkTJuDLL7/Ev/71L9TU1GTkdbFYLHj66adx1lln4cgjj8TUqVPRr18/7Nq1C++++y7Ky8vxz3/+E+3t7TjkkENw8cUXY8SIESgtLcXbb7+NTz/9FPPnzxfeb/To0Vi+fDlmzJiB448/HqWlpTj33HMz+v4XXHABTjjhBPzf//0fvv/+ewwbNgz/+Mc/cPDgQQC58ToR3ZgCZqARRJdCngbP+Mtf/sKPHj2ad7vdfFlZGX/00Ufzd911F797926e53n+iy++4K+44gp+wIABvNPp5Hv27Mmfc845/GeffSZ5n48++ogfPXo073A4NFPif/7zn/MA+B9++EF1riwt+ssvv+R5PpbKfO+99/KDBg3i7XY737t3b/7iiy+WvEc4HOb/+Mc/8sOGDeMdDgdfW1vLn3XWWfznn38ujPH5fPy1117LV1RU8GVlZfyll17K79+/XzUNvrGxMWluO3fu5CdNmsRXVlbyFRUV/CWXXMLv3r1b8Ttv27aNnzx5Ml9bW8s7nU5+8ODB/C233MIHAoGk9z3yyCN5i8XC79y5U/V3EcPSsdmfzWbjq6ur+TFjxvAzZ87kt23bpvi6zz//nB8zZgzvcDj4AQMG8AsWLFBNgz/77LMV30MtDV6e8v/uu+/yAPh3331XeCwcDvO/+tWv+N69e/Nut5s/7bTT+G+++Ybv0aMHf9NNN+n67jyfnAbPWL9+PX/hhRfyPXr04J1OJ19XV8dfeuml/OrVq3me5/lAIMD/4he/4EeMGMGXlZXxHo+HHzFiBP/nP/9Z8j4dHR38lVdeyVdWVvIAhJR4tTR4j8eTNEelsgONjY38lVdeyZeVlfEVFRX81VdfzX/44Yc8AP6FF17Q/f0JgnqBEQTRLRg1ahSqq6uxevXqQk8l77S0tKCqqgq/+c1vcO+99xZ6Onnntddew6RJk7BmzRqcfPLJhZ4O0UUgDRBBEF2ezz77DBs2bMDkyZMLPZWc09nZmfQY01Cdeuqp+Z1MAZB//0gkgkcffRTl5eU49thjCzQroitCGiCCILosGzduxOeff4758+ejT58+uOyyywo9pZyzfPlyLF68GBMnTkRpaSnWrFmD559/HmeeeWZReD9+/vOfo7OzEyeddBICgQBWrFiBjz76CL/73e8Mlx0gihsygAiC6LK8/PLLmDt3Lg4//HA8//zzRdFl/JhjjoHNZsMDDzyAtrY2QRj9m9/8ptBTywunnXYa5s+fj9dffx1+vx+HHnooHn30UdWkAIJQgzRABEEQBEEUHaQBIgiCIAii6CADiCAIgiCIooM0QApEo1Hs3r0bZWVlVFiLIAiCILoIPM+jvb0dffv2TdkPkAwgBXbv3o3+/fsXehoEQRAEQaTBjh07cMghh2iOIQNIgbKyMgCxH5B1gSYIgiAIwty0tbWhf//+wn1cCzKAFGBhr/LycjKACIIgCKKLoUe+QiJogiAIgiCKDjKACIIgCIIoOsgAIgiCIAii6CADiCAIgiCIooMMIIIgCIIgig4ygAiCIAiCKDrIACIIgiAIouggA4ggCIIgiKKDDCCCIAiCIIoOMoAIgiAIgig6CmoAvf/++zj33HPRt29fcByH1157LeVr3nvvPRx77LFwOp049NBDsXjx4qQxjz/+OAYOHAiXy4UxY8Zg3bp12Z88QRAEQRBdloIaQF6vFyNGjMDjjz+ua3xDQwPOPvtsjBs3Dhs2bMDtt9+O6667Dm+99ZYwZvny5ZgxYwbmzJmDL774AiNGjEB9fT3279+fq69BEARBEEQXg+N5ni/0JIBY47JXX30VF1xwgeqYu+++G2+88QY2btwoPHb55ZejpaUFK1euBACMGTMGxx9/PB577DEAQDQaRf/+/fHzn/8cv/zlL3XNpa2tDRUVFWhtbaVmqN0cfygCu9UCqyV147x0CYQjsHIcbFaKOBMEQbR2htDWGUJFiR3lLntW39vI/btLrcgff/wxxo8fL3msvr4eH3/8MQAgGAzi888/l4yxWCwYP368MEaJQCCAtrY2yR/R/fEFwzjlgXfxs7+uzdlnBMNRnD7/P7joiY9y9hkEQRBdib9v2IVTHngXd7/8VUHn0aUMoL1796JXr16Sx3r16oW2tjZ0dnaiqakJkUhEcczevXtV33fevHmoqKgQ/vr375+T+RPmoqHJi8b2AD7f1pyzz2jsCGBncye+3NmKcCSas88hCILoKnQEwgAAj9NW0Hl0KQMoV8ycOROtra3C344dOwo9JSIPNHtDAIBAOIpoNDeRYH8okvj/MBlABEEQ3rgBVFpgA6iwn26Q3r17Y9++fZLH9u3bh/LycrjdblitVlitVsUxvXv3Vn1fp9MJp9OZkzkT5qXZFxT+PxCOwu2wZv0zOoMRyf8X+oInCIIoNN5AbF30OLO/5hqhS3mATjrpJKxevVry2KpVq3DSSScBABwOB0aPHi0ZE41GsXr1amEMQTBaRAaQ2FOTTQJhkQcoR59BEATRlWAeoBJHEYfAOjo6sGHDBmzYsAFALM19w4YN2L59O4BYaGry5MnC+JtuuglbtmzBXXfdhW+//RZ//vOf8eKLL+KOO+4QxsyYMQMLFy7EkiVL8M0332DatGnwer2YOnVqXr8bYX6afSHh/ztzZJx0BhNhLzKACIIgAG+QQmD47LPPMG7cOOHfM2bMAABMmTIFixcvxp49ewRjCAAGDRqEN954A3fccQcefvhhHHLIIXj66adRX18vjLnsssvQ2NiI2bNnY+/evRg5ciRWrlyZJIwmiOY8eIAkGqAQaYAIgiA6hBBYERtAp556KrTKEClVeT711FOxfv16zfedPn06pk+fnun0iG5OSz48QKL3zdVnEARBdCV8LAssB7pLI3QpDRBBZBOpByg33hmpB4gMIIIgCEqDJ4gCI9YA5SMERh4ggiCIhAaIDCCCKBDiLDBxuno26SQPEEEQhASWBl9oETQZQETR0uwVhcDCufIAURYYQRCEmEQaPGmACCLvhCNRtPnDwr/z4QHK1WcQBEF0FcKRKALxqvjkASKIAtDSGZL8O1dtKqgVBkEQRAIW/gJIA0QQBUGs/wEAf468M37yABEEQQh0xAXQdisHh62wJggZQERRIs4AA3KZBSbSAOVIZ0QQBNFV8JkkBR4gA4goUsQCaCCXrTBEITDyABEEUeQINYAK3AcMIAOIKFJakjxAOdIAhakVBkEQBMMsKfAAGUBEkdLsy78HiAohEgRR7LAiiCXOwqbAA2QAEUUK0wBxXOzfgVxpgMJUB4ggCILBagCRB4ggCgTLAqstdQLInXfGTx4ggiAIAS9pgAiisLAQWJ9KN4D8dIMnDxBBEMVOR1wDRCEwgigQLATWt8IFID/NUEkETRBEseMLUgiMIAoKC4H1jhtAnTkyTjqpGzxBEIRAB9UBIojCctDLPECxEFiuRNABaoZKEAQhQCJogiggPM8reICyb5xEojyCETKACIIgGN54YkihO8EDZAARRUhHIIxwlAcA9K3MnQZI/p6kASIIotjxUgiMIAoHqwLttFlQWeIAkJtGpXKvEmmACIIodigERhAFhKXAV5U44LLH3LDigoXZQu4BikR5hCLkBSIIonhhafDkASKIAsBS4CtL7HDHDaBgOIpIPCyWLZgBJI51kxeIIIhihqXBe0gDRBD5p0XiAUpcAoFwdo0Tpvkpd9mFlhskhCYIopghDRBBFJBmb8wAqvY44LKJvDNZ1gExb4/bYRU+xx+kEBhBEMVLB2mACKJwiENgFgsHpy12GWQ7PMUMKpfdCreDaY3IA0QQRHESjkQFzzilwRNEARCHwAAkhNBZTlNn4S6X3QIXM7JykG1GEATRFfCJNpkUAiOIAiD2AAEQhNDZ1ucIITC7Fa74bodE0ARBFCtM/2MTed4LSeFnQBB5pjnJAxS7DLJtALE2GC67SANEBhBBEEWKWADNscyQAkIGEFF0CAaQJ+YBYiGwrGuARB4gQQNEBhBBEEWKl9UAMoH+ByADiChCmr0sBJYfDZDTbhF5mSgLjCCI4sRMKfAAGUBEESIXQbvz4QHK0WcQBEF0FTrIACKIwhEMR4VuxFUlLASWGw2QX6wBypHQmiAIoqvgZVWgnRQCI4i8w7w/Fi5WoRlAzvQ5fnEWGHmACIIochIaIHN4gMwxC0IVnufhD0WFmzSRGYkUeAcsllgWQq4ytPyiStBugzqjzmDE0DEPRaLgecBhILX0oDco9OURY+E49KlwmSJLwywYPR5E1ycQjsBmscBq0X8d+EMRYbNDJGOmTvAAGUCm566Xv8IbX+/B2zPGom+lu9DT6fKwDDBWAwhAokZPlttUMG+P02YxFGZ76bMduPuVr/DIFaNwzjF9U46PRHlMeOh9cByHt27/ia4F+82v9+CW574Ar9L/9cJj+2HBpSNTvk8xsHFXKy7880e44SeDcWf94YWeDpEH/KEITnvwPfSpdOOVaT/S9Zrln27HPa9uxJ+vOhb1R/bO8Qy7JiSCJgzx+fZm+IIRbNzVWuipdAvkAmgg4QHKVSsMqQco9Wd8sb0FUR54/7tGXZ+zs9mHHxq9+H5/h2DgpeLTrQfB84A1XpCM/TmssSXh823Nut6nGPhqZyuCkSj9JkXEzmYfdrf6sX67/mO+fnsLIlGezhMNOuIhsBKTaIDMYYYRqrBienpvbIQ2LARWJfIAuR05EkGH4yJomxVOpgHS0QqD7ZK2NHp1fY54XLM3iJpSZ8rXsIawd084HDf8ZIjw+Dd72nDWwx8IcyASx4P6uBUPB+OlMqJ8zMOqx6sajETjr6W1Wg0Wci81iQaIPEAmh3kl2I2byIxECCzhAcpVKwy/ggdIj5eJLRINTToNINE4veeJWAslhsXmmViRSGSuUB+34kG84QxF9IXGQ5FYPLmFNquqUBo8YQi/YADRRZUNWhQ8QLlKUWceA5fdYqjYIlskDniDaNVh0DQ0dQj/r/c8UQoFAokOzZ2hCCJRFYFQkcE8QIEwFbEsFsRGjN7jHgzTZjUVCQ2QOUJgZACZGJ7nBY9Bi5cuqmzA3NNiz0fOWmEEmQFkNRRmE3tfGg6k9gKJPUV6d59ska722CWPi3dmXoUMsWKE6RbIA1Q8iI0Yox4g2qyqw2qwkQeISEkwntoM0EWVLRRF0LlqhRFOGEBGUu3FhofYu6NGQ2M6IbBkQxCIZazZ4noHH4XBACRCkqQBKh7SC4HFxrWQB0gVygIjdOMXpWXTRZUdFEXQOfMARYX3dzn0f4ZYgNyQQgjtC4axu9Uv/FuPoRyORNHuj32GPATGcZywOHWQEBpA4niQB6h4EHvcgzpDYCxU1uILIkrhY0XMVgeo4AbQ448/joEDB8LlcmHMmDFYt26d6thQKIS5c+diyJAhcLlcGDFiBFauXCkZ097ejttvvx11dXVwu9340Y9+hE8//TTXXyMniHec5AHKDkqeD1ajJ5BlA4i9n8tuLA1eHAL7IYUQemuTT/JvPaHSls7YGI4DKtz2pOdZp2bKBIvRIdIA0Y2tOMjEAxTlIWwwCClCGrxJiooW1ABavnw5ZsyYgTlz5uCLL77AiBEjUF9fj/379yuOnzVrFp566ik8+uij2LRpE2666SZMmjQJ69evF8Zcd911WLVqFZYuXYqvv/4aZ555JsaPH49du3bl62tlDfGOk4R12UEQQXvy4AFSaIWRKszG87w0BJbCAyTPFNNjKLMwYLnLrpje6xEywWgRB6QGKQmhiwOxxz0Y1mf0ig0l2rAqI6TBkwcIWLBgAa6//npMnToVw4cPx5NPPomSkhI888wziuOXLl2Ke+65BxMnTsTgwYMxbdo0TJw4EfPnzwcAdHZ24pVXXsEDDzyAn/zkJzj00ENx33334dBDD8UTTzyRz6+WFcQ35BZfELxa2V5CF9EoL9z8q0UeIGcONEChSBThuLfAZbfo9gB1hiKS6swNTV7N4840QsxroydUqhQGFCMYQBTyASDVZFEz2+JAbMAEdXqAxKEyMoCSiUZ5+EgEHSMYDOLzzz/H+PHjE5OxWDB+/Hh8/PHHiq8JBAJwuVySx9xuN9asWQMACIfDiEQimmO6EuLFNhzlSZORIe3+MFgEQ6kOUDY9QOJjF+sGb9H1GewYc1ysSnNnKIJ9bQHV8awG0MgBlQD0LbzNCplwYliKKnmAYoh/B2pmWxxkkgUGkGZTCfFGoug9QE1NTYhEIujVq5fk8V69emHv3r2Kr6mvr8eCBQuwefNmRKNRrFq1CitWrMCePXsAAGVlZTjppJPw61//Grt370YkEsGzzz6Ljz/+WBijRCAQQFtbm+TPDMgXW7qoMoMZBx6HVdI0VOjTlUWPBzt2HBfLrBJ7gLQ8OuJuyQOqSwAAWzQywVgV6NEDqgDoDYGl8AA5SAQtRhwCIwOo+8PzvKScREh3HSDyAGnBvD+W+JpoBswxC508/PDDGDp0KIYNGwaHw4Hp06dj6tSpsFgSX2Pp0qXgeR79+vWD0+nEI488giuuuEIyRs68efNQUVEh/PXv3z8fXyclAVlIhkqsZ4Za6jfr8p3NNGd27Fw2KziOE8JsUV7bpS4uFDaoxgNAvSI0z/PY0hgzjkbVxQygFl8oZai0WaUIIqOUNEACck0WhcC6Px2BsBC+BoCA3hCYRANEm1U54irQHJe6tUg+KJgBVFNTA6vVin379kke37dvH3r3Vu6kW1tbi9deew1erxfbtm3Dt99+i9LSUgwePFgYM2TIEPznP/9BR0cHduzYgXXr1iEUCknGyJk5cyZaW1uFvx07dmTnS2aIfLdJu4rMUBJAA4lmqKEIj7DOxS4VggA6blwxDxAgLW8gR1wngxlAaj3Bmn0htMWzTUb1rwQQC5W2pzBcDqoYggzSACWQa7LIAOr+NMsyKfV6gCQiaNqsJmG2FHiggAaQw+HA6NGjsXr1auGxaDSK1atX46STTtJ8rcvlQr9+/RAOh/HKK6/g/PPPTxrj8XjQp08fNDc346233lIcw3A6nSgvL5f8mQH5YkshsMxQ83y4RSmZ/ixl+bBj54q7eu1WDizhSsvTxLwNHoctpQeICaD7VbpRWeIQQnmpUuHZ82ohsBLSAAnIw4DZLpZJmA/5RpNE0NmBXUtmSYEHCtwNfsaMGZgyZQqOO+44nHDCCXjooYfg9XoxdepUAMDkyZPRr18/zJs3DwCwdu1a7Nq1CyNHjsSuXbtw3333IRqN4q677hLe86233gLP8zj88MPx/fff4xe/+AWGDRsmvGdXgjxA2UWpDQYgjUd3BiNZ2aEIbTDiFzvHcXDbrfAGI5oF9VidDI/TisG12gYQ8wwxQ6mqxIE9rX40+4IY0KNE9TOEUKBHJQTmoBAYQ94Uloohdn/k66zROkAAbVaVYJXlzeQBKuhMLrvsMjQ2NmL27NnYu3cvRo4ciZUrVwrC6O3bt0u0O36/H7NmzcKWLVtQWlqKiRMnYunSpaisrBTGtLa2YubMmdi5cyeqq6tx0UUX4be//S3sduXdrpmR7zYprpwZauJfjuPgslvgD0WzFuJgniQWXgNi2WDeYETTA+QTuYkH15QCALYf9CEUicJulTpsmWHEDKBKkQGkBfsdqikElhK5EUjtMLo/cuMlpKMOEM/zkiww2qwmI3i3yQBKMH36dEyfPl3xuffee0/y77Fjx2LTpk2a73fppZfi0ksvzdb0CkpyCIwuqkxQE0EDMY1ONg0g5ikQh9eEpquaHqDEItGr3Am33YrOUAQ7DvowuLZUMlbuAWKNTVPtPhOhQOVNAYmgE8h/A/IAdX/kxoseEbQ8TEab1WQ6TNYHDOhiWWDFhl+USg3QRZUpWunf2W6IGhAaoSYuMSHbTOMzvEKp+FimhJYOiD3GQmXMsEu1+2TnkZoImmmAKA1eWrsEyJ5GjDAv8nVWjwha7P0BaLOqhJDgYSINEBlAJobtNmtLnQDoosoUrfTvbBdDFDxAdrEHKF5vSOMzvEKp+NjrBqnogKJRHg0H4gZQPFTGDDstQ1lc40SeDcegVhgJ5BqgbNaKIsyJfJ3VowGSG0kUAktGqHFGHiBCD+xm3KfSDYAuqkwRWkAoiH+dBpqV6oG9j1NkAOlph+GVuYkHxz1AP8hS4Xe3diIYjsJu5dCvKnZ+MMNOy1AW1zhJVQfIRzf7ZA0QpcF3e+QbCD3d4OUhsGyG07sLlAZPGIKFSvpWxFp7yOtTEMZo0dC+uHW2qtBLZ/zYST1Aqb1M8kUiEQKTVoNmHqG6Hh6hoSkLaWkVzGRhQJfdIsxHDktTpRBY8m9AlaC7P2ydYNegHg8QM5KcNgts8euRNqxSmHe7xEEGEKEDtoPoHTeAKASWGVohMFeWPUDsRinWAOnRGXWINEAABOGzPAQmF0ADCcNOSwSdqgo0QCJoMUlp8GQAdXvYNdKzPCY9CEZSZ4ExD5DDZkElC0XThlWCV1TiwyyQAWRi2M24b0UsxOENRgRxLWEMfygiGB6Vih6g7BpAgZCSBki/B4gtEoN6xAycfW0BiUEiCKAlBlBqEXQqAXTssxMhsGg09eLfnfHJRdBUCLHbwwyXnmVxA0iXCDrhAarUEYouRigERhiC3Shry5xCFWEqsJUezCiwWTjFC1BPiroROhUMILcOEbQvKF0kKkrs6BHXLIm9QFtkGWBAwrDTOke0woAM8e/jK3KPR4csc4V0Hd0fdo30LIt53vWJoGMbBbvVoisZoRihNHjCEGyxdTusulOcCWXYrq6yxKHYiE8IT2W5FYZTwQOkdRNNlItPLBJKqfBMEzSoJlEbSI8HiOmDtEJgTptFMLiLPQzGvn9N3BtABlD3JhCOCAVAe5Xr9wAFI7HXxAwgWquVSBRCpBAYoQOxkJbiypmRyvPBtDrZ8wAli6DdOrxMXoVy8fKmqIFwBDubOyXPAQmjxqcRKk2EwNQ9QBzHCbu0YhdCM00W88KRBqh7w7ynFg7oES8/ok8EHfMAOWwiA4gaokpgrTA8JIIm9BAQhLRWXSnOhDoHU4h/BQ1QljRWftGxYzh1fIbSLilRCyjm9dl2wAeeB8qcNtSUJr5PmcuWMlTKzp9qlT5gDCEVPlDcN3wWkmQ3Q/IAdW/E1eJZj0A9zVCZkWS3WlDpoRCYEhQCIwwh1pFQXDkzUnk+hCrNWfIAJcKXokrQggdIeUHleV5RKMgKHbIQmJABVuuRhPMsFi6l+12PCBoAeYDiCCGwUuYBIhF0dyYRKrfDwQwgPSGwcCILjDaryshrnJkBMoBMjF+USk0aoMxoSaF9yXYrDOHY2RRE0CoeIH8oCpZ0VSI2gOIeoC1NXvA8n9QEVUyqUKkeETSQEP0WuwaIGYA9PDEPUIA8QN2aFpGnmDUf1iWCZmnwVk60WaW1mhGN8oK2ijRAhC46RWEUFrKgXUV6CJ4PlfYPelLUjSAcO4VmqGpeJrG3pUQUOhtQXQKOA9r9YRzwBoVQ2GCRAJqRaveppw4QIO4IX9wGEKuGnfAAkQHUnWkW9Qt0CAaQ/jpAdqt4s0reeob4uqE0eCIlPM8L3ohYFhiFwDJB0L6oeoBSp6gbgR07iQfIoa0BYnoTj8MKiyUR2nLZregXb4fS0ORNeIBqlTxA2ouv2MWvRaIfWHHf8AUPEGmAigKxBohCYNmDeZI5TpoYUmjIADIpAdFFRyLozEnl+chZM1SRB8hp084C0xIJJjLBOhSLIDJSud9b9HqAKAQm0WT1YB4g6o/WrRGHiFkITJ8ImuoAaZGop2VTLENSKMgAMinihdZlo4sqU1KJoLPdCkOs32IIHiAVnZFWt2Rm7GzY0YqmjtgiPVDJAPKop+AGw1EhDq83BFbMImixJquGeYCyVCeKMCfiJAG7NXaj1pcGH7uuHKIQWJs/hEiRV1Jn+Eyo/wHIADItLExit3KwWUkEnSnCzk4l/dudIxG0pBWGTTvMJm+DIYZ5gN75dh+AWJl+pVi6VqiU/QYWLpYyr0WiI3zxGkBi/RPT4AXDUbqpdWPEHlIjITDmARL3AuN5oLWTNqyAOVPgATKATAvzADHPBBXXygyxuFEJZxa7wfM8LxGwM5gHSO0zhBpACoXCWFPUfW0BAMoZYIC2CFq8uxVrjJRIeICKN+TjFapyWyXHhHRA3RdlEbSeStBMBM3BbrWgLH790IY1hhn7gAFkAJkWQUQrGECxG3drZ6joG1QaJRLl0ebXrn+TzWaooQgvhE5cBlphaNXJkBs8zCCSo6UBSgg8tQXQQOymL55TMSLetbKieAAZQN2ZZpGn2G4kCyycyAJjrwdIs8noEG0mzAQZQCZF3kyT3bijPISbOaGP1s4Q+PgaVunOvQZI7OFRbIWh8hkdGhqgvpVuwSUPKAugAYg6USefI806+oAxSoUssOI1gATdQjwrjxlBlArffRFfI+x6CxjoBs9eU0WtiySwa4k8QIQuAjIRrcNmETJzSAhtDNYAtMxlg82qfMpnUwPEjp2FgyCkBBJhNn8oCp5P3lX6BDdx8i7JauEwsEeJ8O9UITBlD1BIMkYLqgOUrFtIJWInujbRKC9odsRZYMYKIcZewzYiB8kDBMCcVaABMoBMi9wDBICE0GmiJ/U7m4UQxcdOnPIpPpZKu8oODQ0QIDV6lGoAAUCVRz1U2qyzCjQg9gAVr7dDvmizmk4UAuuetPlDQuha0gvMYB0gIHGNUQgsRiIEZi4DyFyzIQTkGiAglomyq6Wzy15UncGIpC5OKvyhCBxWS0rBrpj9bf6kuh2sd5bWjZ8ZJ5Eoj1AkKuz+0kHp2Mn/7Q9Fkp4XRLcqu6RBNaUA9sFq4dC/qkRxTKVbGioVa55SZcKJIQ2QyACK/xYJD1BuDKB2f0g1a6hXucvQOWn0WgtFouB5SMKs2SYS5bGntVPxuTKXHRUq4el8wTykHocVDpvFkAcoKKoDBKQuSNpV8QXDgkddTk2pM2lNS7yOhcDMpQEiA8ikKGURperzZGYeWPkt/vL+Frx688k4+pCKlOMPeoMY9+B7GF1XhWeuPl7XZzz2zmY8+O/vVJ/XagDqEjUt7QxFMjKAlI4dEFscbRYO4WgsS6xS9jrmbVFbJFhPsP5VbtUblcNmQanTho5AGM0+qQGUqhaSGKoDlFyXKZcaoO/3t2PiI2tUvQ2H9SrFytt+omsz8PpXu3Hr8+vx4CUjcOGxh6Qcz/M8Lnj8Q7T5Q3h7xlihYGe2uXLhJ1jbcFDxOZuFw/IbT8LouqqcfLYexFWggUT4OhzlEY3ymr+9uBs8kLolTVekqSOAcQ++h3a/8prQq9yJd+88VdHLQ2nwhCGUQmCpOn2bmXUNBxGO8vjwhyZd47/c0YLWzhA+2Nyou+7Ke/9rBBBbuJw2i+SvzGnDOcf0UX2tw2oBi1ZlusNXKoLI0NIapYqTjz2sFoNrPLj0+P6an88MHPlOTW8VaEBcB6h4wz3y1N1caoA+3nIQwXAUFg5J5y4AfLevAy06a8p82nAQUR74fFuzrvGBcBT/3d2GHQc7hSrj2SYS5fHp1pjx45B9PwsXMzLWb9c331yR8JDGrh/xJiNVNeikEJin625W1fhub7tg/Cido/vaAvh2b7via82aBm+u2RACchE0II4rd72LiokBtzR26Br/Q3xcKMJjZ7MPdT2UNS9itsQX71dvPhlH9UvtZRLDcRzcdit8wQj8wcxucILxqhCCcNqtaA+EFVsqMMGx2iLRq9yFd+48NeXnV5U4sLM5OVSaqhaSmJK4F8obDIPneVOVr88XgiZLpgHKhQeIXRfXnTIY90w8QvLc0XPeQnsgFnqo1hG+PBg/znrDl2Iv35ZGL4b1Ltc7bd20dSb0NRvvq5cYF7P/vhF/+3hbwTd2zFipEjxAiTmGIlHV8A57Hoh1gwe6p16TnScj+lfi77ecLHnuyoWf4KMfDmBLoxfHDkj24nlNqgEiD5BJUeol1ZUvKma06d1hisdt0fGaFl9Q8HioZUilIltCaNbt3aUQSnDHQ21KDVFZGnymi4RaNWi5i18LZoTxfPF6gXyBRBo8kFsNkNDgVuHcrfQYE9SycV6dx80nErrnygPEzr1Spy0pfGsWvYz8+nBIDCBtL3RSHaAuvFlVQ0vHw87bhiblDW4inGwuDRAZQCaF3SDF8fiuelFFo7ywKKdjADU0pn4NG9+r3Jl2nDlbxRDZsVPyAAmZREoeII1WGEZQ0x+0GEiDd9utYJKHYk2FT8oCE8oY5NcAqjJoILAbeboeoFygpT8zS8ZUi8xDarFwsMUvglSZYMGkOkBdd7OqhlYmV8IAUj5/Unm3CwUZQCalMx6GEd9EWfaOmgrfrLT7w4L7u6kjqKs/jsQA0mE0ad1A9JKtdhjs2CmJSbXaYfiyFCdXqgYtNkL1hMA4jhPS8Ys1FV4u3BQ8hFn2iAXDUew46AOgXODS6M2UhXL0GkBiA1dtB58pWvqzaqGBr7k8QAB0Z4LJRdCVos2qUs2vroiWjoclaKgZ0GZNgycDyKQwL4I4jNJVQ2Dy+W5NYdD4gmHsafUL/zZiAKm1iNBD1jxAGhqgRC0ZhTpAWVoklEIKYiNUTwgMEOmAijQTTOjNFv8dXFkslilm+0EfonzsxlJb5kx63qiHxGgITHx8cxcCi3tXFDRMZlnX5B4gIJEJplcEzQwgoXluJNptQshejY7ug2ti6+7WA17FVk1mFUGTAWRS/IIGqOuLoOULW6pFVv68HuE00wmptYjQQ7baYQhp8Aqp6i4VDxDP88ICky0PkPiGyY4Bq3Gih2JPhRd0C3GDNFUrk3QRey+VxOZGNDLBcFQ4j3R7gEQevmZfKCcNl7W8j2ZZ19g1IhaaO+IbllQhMKYRYhlRbnviOiu0YZcttLJUD6lyw2bh4A9FsbfNn/S8jzRAhBEED1A3SIOXL2ypDJoGmTGzu9WfMuzAXK+ZhMCy1Q4joOkBUtaRBMJRId0/Yw2QQkjBiACakUiFL1YDSLprzZUGiF0Pqdqb6PEAicfoNVzlhpKepAOjNGuEwMyyrjFpgfgaYVldRkNgHMeZxrDLFonCoMkGkM1qwYB4qx55GCy2uSMPEGEAdsNXKoQYCEezrkPIJfKFLdUCy0TPowZUCd956wH110SjvBBWy8QAcmVLA6RQw4mhlkkkvgllGgJTuqEo7W5TwRa6jiLVAMl3vNkKkcpJpV8zUlNG7CXyBsK69CdyQykXYTAtEbRZ1jWlEBjz4qQygOR1gGLvYw7DLlukKmY4WCUTrDMUEcLvalXuCwUZQCaFeSHEN9FSp02ISXeli0pcYh7QHwIbXOtJmV0AAPva/egMRWItIqqVW0ToIVshMHbsnEoGkMpnsDCE226F1UDrDyUSHgORB8irvwo0w1P0GiCp2z5b54ecLaLzXQkjGhmJ8J3X18lc7uHLhRBaSwRd6rQJ2VaFXNeUvFTMo5PqdwwKHqDEtatWkLSrkqqdBVur5RtccYi1RKOWUiEgA8ikKLVT4DjONIJBI7DFb+SASgAxY0ZrZyrW8zBxnZYBxDxGA6pLMmphka06QFoeILXPyGap+EpRFhj7nbVCEGoIHeGL0ADieV4hDT73GiAljIRS5GEyPWEw5uFjdW9y4gHSMMDNsK51BiOCkVMpEUEzD5C2J00eAgOUNyJdmVRJGoNU1mpxTz0jfR3zARlAJsWvchPtinFltqgdc0glrBYOvmAE+9sDimN5nk9oImo9wq74Bw3dUDYE0ED2NEBarTDUMol8Qow88x0S0wAFwlHhZq3k3k9FwgAqvhBYIBxFWNBkyUNg2csCa/eH0Bi/FlJpgPR5gKTrgh7jlY05vHcZgNzUAkplgBd6XWPzs1k4iU6FhbRS1gEKs9IXiWu+0EZdtkmVycXWarkBZNY+YAAZQKZF7SbaFS8qtvvrWeZE/yo3APVF9qA3iLZ4v5mBPfSFwLJRAwjIXqVfNeMVUNcZZXOR8DisolBpKP5f4yJoFrIsxkKIYsOBaaFy4QHa2hSr/1Nb5kSZS9k4NVJTRr4u6DFe2Xdl7WPUUpkzIbUBVNh1TXx9iDPxHLrrAEm7wQOFN+qyTapehWwDuuOgT2IwpnpdISEDyKSodRSvUmlzYGbEi18iTqzs0WHGTL9KN1x2qy4DSOwxygS1DC2jMA+BUu+gVBogpQwLo0hCCnH9QSYeoGJMg/cFExsQpsliJSkCWTSA2HWgZbwz4yAYiaas7SNPYddjvLIxh/cqhd0aS2Xeo5DKnC48z2uKoMWPF2pdU7s+7DZ9WWBCHaBuLILWqgMExIx4j8OKKB+rbcXwpXhdISEDyKSo3USFuHIXEtaJFz8hTqziAdoi8+YMjDdBbdGoT5ItD5BQoyfDTBQ141X8WJIBJCu6lyny3adghBrIAhPS4IvQAOpQcPfnohkq84RqhW9LHFbBE5GqRo/cgNBjvDLju9xtx4B4EoGe9jN66QxFBANB7fyrkhns+UbNQ+XQIYLmeT7RCsMqDoF1vc2qFlpp8EBs4zVIqAid2OB2pHhdISEDyKSodRRnO/uDXWhXIc4AGaQSJ2Y0yDJi3A4r+la4ACinzwfDUexo7oy9pib9KtBA9m5wnUF1DZCajiTbbmJ5qDRhhBoXQRdjGrzS8XA5sq8B0mO8xzx6+sIpchG0Hg2QOPzKKqlnMxOMnXt2KyeEVeWwhq+FC4Epe6j0tMIIi8KFDkURdNdZq9WIRnmRJ0d9jVISQlMIjDBENMoLOyZ5NeGuGFcW764GpwhpNSgUNEwsysmv2dHsQyTKo8RhRa/y5DYCRnBn6QYnNENV8AAJ/caC8hBYdndJ8mrQRvqAMUqYBqgIPUDM3S/OeMmFB0iv91Lol5XiZir3Nvh0GK8+UZG6wSqpzJnQ7FXW14ipLnDGFPOoyz1ALKQV0vAAifUuLGQGiAqSdgMDyCc657WKGSpJFkgETRiC3UCBZA9QV4sr+0MRwaCo9NgFz872gz7FXZWSJkLQDSlkgokNJrXFVS/ZqvTrVyhiyVBrp9ARSL27MoK8g3g6afBsoStmEbQ4K08wkLNUrI/ned097MSlDbRgz7OeYkZCYB6nTXStZc8AYkZNtca5V+h1TfAAeaQbBGfco6PVC0y8jjmURNAFbvKaDdj1YOGUPduMIbXJBnSq+kGFhAwgEyL2QLhs8hBY14ori9NLy5w29CpzwW23IhzlsTMeumJEojy2HmBdsRM3BC0hdLb0P0AWm6HGd4SKrTBUPiObafCANAQmMULTSoMvPgNIadcqGMjh7BhAjR0BdATCsHAQtDdq6K0pw57vVxnLtjQUAnPoSzowSiLDSv3cK/S6xjykciNNTx0gZhxxHCRFTNkxaw+EU4qozY5Yx6O10VQ6f7Lt3c4mBTeAHn/8cQwcOBAulwtjxozBunXrVMeGQiHMnTsXQ4YMgcvlwogRI7By5UrJmEgkgl/96lcYNGgQ3G43hgwZgl//+te6SsKbBeYdcNgsSYWjmFu1q8SVEwXQYu5vi4XDQJWS6btbOhEMR+GwWtAvni4PQFM3lK0aQECicnPWNEA2BQ+QSqp9tt3E1Z5EqJTdgOxWzlAvHrZgFWMdICXdAjOQQxEe4Szc0JiXpX91ScoGtXrKX0SjvLAuHBK/flJljckLPrJrbWezD4EsGXpaVaAZhV7XDqqJoHXUARLaYFgtEuOg3G0H+2dXkiwooVfHw9b2xvYA2v2x7ywUUKQQmJTly5djxowZmDNnDr744guMGDEC9fX12L9/v+L4WbNm4amnnsKjjz6KTZs24aabbsKkSZOwfv16Ycwf/vAHPPHEE3jsscfwzTff4A9/+AMeeOABPProo/n6WhmjVUem0NkSRlHSngxWcbMzA6euR4lkJyXWDcnrk2QrBR7IjgeI5/lEI1uHcRF0thYJ8Q3zoA4NhhLF3ApDEHyKvHjikKZfR4uJVBjxXurR/rX7w0LPJbaBSHXsghFpwcfaUidKnTZE+Vg9l2zAvDpVHnUPkFDeo2BZYNoiaO0QWOz3c8iq0FstHCrcUi1eV8Wrs5t7ucuOmtJY+JWd3xQCU2HBggW4/vrrMXXqVAwfPhxPPvkkSkpK8MwzzyiOX7p0Ke655x5MnDgRgwcPxrRp0zBx4kTMnz9fGPPRRx/h/PPPx9lnn42BAwfi4osvxplnnqnpWTIbWllEbKFo84ezsgvNNcLiJ9pZqfWMUbsh9Kt0w27lEAgn1ydJvCazDDBAvUqzEQLhKJiz0UghRLbAZGuREBvK6dQAis0loQHqSh7UbKDkkRNX+c1G005jBlBqDxB7rsRhFUI5qQwgsXfP47CC4zhBp5ctHZCeIpzsuUKta8JGzSMXQcfrAGkYvEIbDAUvHjtuXb0fmJFMLnmiC4mgFQgGg/j8888xfvz4xGQsFowfPx4ff/yx4msCgQBcLpfkMbfbjTVr1gj//tGPfoTVq1fju+++AwB8+eWXWLNmDc466yzVuQQCAbS1tUn+ComWB4jtKACgtdP8blWl+L9QMl22wKp5c2xWi6CRkNeX2J+ijYARsuEBCoj1W+nUAcpyFlizKARmJAUeSCxYUT67qd9dAaWy/xzHZbUjvJ4aQAw9Ghmx0F1vEUv2PZ02C2xxD0a2dUDN3mQvsJzKAq9ranPUI4IWh8DkFFrblC2MrE9yIT1pgBRoampCJBJBr169JI/36tULe/fuVXxNfX09FixYgM2bNyMajWLVqlVYsWIF9uzZI4z55S9/icsvvxzDhg2D3W7HqFGjcPvtt+Oqq65Sncu8efNQUVEh/PXv3z87XzJNtCoJ26wWlLtiJ1JXuKiaFdJL1RZYLT2PUir81vj/15Q6JIZhuqhlaBmBvdZm4RQbs7JjGghHJeG8VH12jCIOgTWn6QESG+DFVg1abdearUxBIKGBS5UBBuirKdMiCuOwUIUvhadKqeBjtjPB9NSgslktKCvQuhaORIX2O/I56qkDJHSCtyWHl7tLLSCvgSxVeU8wqgOUJR5++GEMHToUw4YNg8PhwPTp0zF16lRYLImv8eKLL2LZsmV47rnn8MUXX2DJkiV48MEHsWTJEtX3nTlzJlpbW4W/HTt25OPrqKJVSRgovGDQCErppWyB3dvml7jotcJZSrohedXoTBHf3NIN+fhTHDuxURGQ9MuJ153JWhp87Pdu94fRFPeSGUmBBwCLJVG4rth0QL5AsgYIyF5D1HAkKrQL0BUC01EoUOIBcujzAPmCyTenbHuA9Iigxc/ne10Te5wqZRspuyCC1sgCY20wurMHSKEshBry8ydVC41CUjADqKamBlarFfv27ZM8vm/fPvTu3VvxNbW1tXjttdfg9Xqxbds2fPvttygtLcXgwYOFMb/4xS8EL9DRRx+Nn/3sZ7jjjjswb9481bk4nU6Ul5dL/gqJVjdxQLy7N/9FpbT4VZY4hMJuWw/ELhJ/KIJdLbG0eKUbgtKirFQ0MRNYpd8or+3y1iKV8Sp+XOxp8mY5DV7sEdsW/42NhsAAUSp8kdUC8ioYBkD2GqLubO5EKMLDZbegd7kr5fhEbzf1a57pTKo8joR+K4UBxOpPlYgMPVaCIlvFEPV6IAvV55B9XpnLJoQBGQ4DdYCUQmDdxQNkJJNL7AESZxlmy7udTQpmADkcDowePRqrV68WHotGo1i9ejVOOukkzde6XC7069cP4XAYr7zyCs4//3zhOZ/PJ/EIAYDVakU02nU0DJ0aGiBAvFCY/6JqVsgCA5Ld7NsP+sDzsUWopjT5Rq1kACWKJmYugAakaev+YKYGkPKlZbVwwkIpDqNk200sDpWy38xoCAwQCaGLLBVePQSWHQ0QOyYDe3iSSl0owW6kHYGwakq2WOzO5p0qBKZ0cxpYE9PbNXUE0ObP3BjRq0HTk+qfC7Q8VHoqQQsGkKIIuuus1VoYMWL6V5fAwsXOVVbrCpBWVTcLBQ2BzZgxAwsXLsSSJUvwzTffYNq0afB6vZg6dSoAYPLkyZg5c6Ywfu3atVixYgW2bNmCDz74ABMmTEA0GsVdd90ljDn33HPx29/+Fm+88Qa2bt2KV199FQsWLMCkSZPy/v3SJZAqBNaFUuHV4v/yTAEmbh6sUtFZqT5JNosgArE6OSz9Pt1id1oCdoZTlgkWCEeEVNpsLhIsVMqMTKMhMAAoKdJU+EQITFkDlKkHSNC76SzfUCGuKdOpfN2LDQ0WbtArghYbemUuO3rGK0lvzdALFI5E0R7X16QywIV2H3le17Q8VA5r6m7wWiGwRDsM83vrtRDCWDrWJ6fNikOqWNKK19QeoILO6LLLLkNjYyNmz56NvXv3YuTIkVi5cqUgjN6+fbvEm+P3+zFr1ixs2bIFpaWlmDhxIpYuXYrKykphzKOPPopf/epXuPnmm7F//3707dsXN954I2bPnp3vr5c2qTxAXSmurLa7khc3TKXnYfVJOgJhbD/gw6E9S4UQmN6bSCo4joPLZoE3GEk7zTmVBgiIHdd2f1j4DHkqcraoLHFg2wEf2gNM4GncA6RXS9LdSHiAZBoglUKWRmlQaPmiBasp0+ILocUXQs+y5LCZkgfIG4iVMFCr/6R2cxpU48H+9gC2NHpxzCGVuuaoRItIX5MqUaFQ65qWh0oohKgpglauAwR0nxCYV+V6UGNQjQfbD/rwzZ42oTaVGTVABTfJpk+fjunTpys+995770n+PXbsWGzatEnz/crKyvDQQw/hoYceytIM8w8TWDpTeIC6wkWltruSN11M6HmUw1msPslXO1uxpcmLyhIH2gNhcFyscGK2cDus8AYjGXiA4m0wNAygRCYYM4DC8cctSRqETJD/5vIaJ3ooFUIpxWUAeYPKhgELk2YrBDbYQPi2qsQRq+yt4iFRSoMPR3kEwlFVgzzR9FX6/OBaD9Y2HMxYB8TWqHIFfY2cQq1rWo2ChUKIWiGwsHodoK60WdXCaIh+cK0H//muEV/vahUeoxAYoYvuogGKRHlBQyDfXTFDp6GxQ9IUUquis1gHxMYfUuWGU6HlRLqw90rXA8Re59RoGCik28d1Rmo320yR9zVKLwTGPEDFpQFSW/CZUD7TQoiCwW/Ae5nqZiquZlwiWju0dEBqWqdsZYKxOVXrML4Lta4lKlUreIAMpMF3Zw+Q0WKGbIO7MW4Aue1WSXV/s0AGkAkRdCQKrRSArhNXbu0MCVWR5eGXuh4l4LhY5deD3qBoR6zDAGr0ikII2RFAMxIhjvRE0MxzpOkBkoVRvDkSCcqNzvRE0MWnAQqGo4ImK0kDxDxAGbTC8AXD2N0aq2hupIddqpupONxss1oEvZLWsfOphsBY3a2OpNcYodmrTwAtHpP3EJhCrTKGXUcILCGC1qoDFOrS1dSNtrNg58/3+2PnjxlrAAFkAJkSQUei4tnoKrsKtpMrc9mSBIIuu1XoWL1hRwsOxBchLU2EeFdqpIquETItdJdoY6JhANmkQtoOA0XGjCA3eNIpFik0RC2iEJjYYEjWAMWPXQYeoK1Nsfo/VSV2Q6UJUnuApDfyUmdq/ZbauSfebGRy4zbShqVQ65papiog8gBlWAcoHOUFLV5XxOgmjXk2mf7HjH3AADKATAlbXN0qgtiuEldOVQCNLbJvfxNrftur3KlpBCTqk3RkvQgiI9NWB6y4oZYHSC6kTezCs7tIVIpc+no0GEp4dNaT6U4wg0HcHoKR8AClbwClm72oZSD4QxHBa8mKjnp06LfUxK0DqmMNib3BCBrjhTTTQa3LuhKFE0GrV6pmIuh0Q2Auu1VYC7pC1q4aShXDtehT7pL0zjOj/gcgA8iUGBFBm9mtetCrvftj3pt3vo0Vw0x1Q0jUJwniq50tul5jlEwL3aUyXoFkIW2umgWKf/d0BNCAuCN88WiAEu7+5OMhGK8ZeIDSDd9qaWTYYzYLh7L4vEscqfVbaj2eHDYL+sc7yv+QQUsMI33oxBXu87muadYBihs1AU0RdGyuSiLo2Pt2jQ2rFr6gMS+1xcJJ1mYzpsADZACZktQi6NiFGorwQhaHGUm1+LELZF8ba2iqfUMQ1ydhr8lWCjwj047wzKjRFEHLdEa5ahYor76dDnqbanYnElVvtZrZpq8BMloDiKGlkWEVoitL7ELKux79llZ2TzaE0C0pNkFi2Jh8r2ti8bgcu546QJHYXJU8QLH3LUyBx2zB87yoMrp+L7X4/DZjCjxABpApSdUKw+2wCu5FM7tVtdJLAWCQrAnkEB03BPGuwmGzoG+FO4MZJpNpQ9RUxiuQXEwvV71yxAt6OgJooDjT4LUM0my0wtAj+FdCKwTWorDZ0GO8JppcJp972RBCC5sgHR5It90qhJzyta7xPJ9YpxTm6NQlgo7XAVLzAMVDkmbXbKrhC0aEZBYjmzTxWk0iaEI3eqoJi7MLzEqqLtDyG4CecJZ4VzFIZxsBI2QsgtZRCFHeTiFX3ZLFHqB0UuABcSFE83oas41W5dpMNWI8zwsCfiMp8EDCiD2oYBwwrY249AE7dj5NEbSGB6g2Cx4gAyJojuOEcfla17zBRBV2rTpAWq0wEiJo5bVITx83M8OuB45Lrhelhdijn23vdrYgA8iEMPe61k2U7e4PmnhXkUoE3bfSLdk16TGAxGOyrf8BsiCCNlAIsVNuAOU0BJaeB6gYW2EIxQEVjIJMW2E0+0JC9/GBPQwaQB71TY9SGEfQb2mEk3waNajkxUrTQZ6ZloqqPIeLmKfJYbMoXrOCARTRyAKLqGeBARAZdeZdq7UQt8FQqyiuBHmAiLTQ40XoCqnwbMfDXMByrBYOA+NVnK0WDv2rU1d0Fu8qjO6g9SAYJ+kWQkwRvgSSjaxcpcGLQ6XpeoCEEFgxGUAaWXns2AXS1ACxcFK/Srfm9a2EcM13JteUaVGoZWMsBKZgAMWvr+0HfJoaGC209DVK5NsAEnuolG7u4lYYasJs5h1SC4FVa2i3ugJG22AwBktE0ObUAJnTLOumBMIRHOgIggeEGjhKpNIAAQmjYmuTDzubfbrn0LPMpXqhGsEfisBps2juCPRkgAyq8eC7fR0YUF2iuoOSj1f6/2whhKcybIaqHQJjYbbYwpnYhWd/kagqcWBvmz9tDVBXEEF3BiOaWXdyolEewYh6e4iOHGqAhPBXGucuMyJiFdbDkrpOgqEh2mykMl6D4ajgvVDqQderzAW33YrOUAQ7mzsNz1mir9HrAYrPP5UGyB+KoKlDOT2/h8ep+3xI5aESr0mhCK9Y7DCkkQYPpCeC5nke/lDU0HltFL2foXU9aFHlcaCyJNa/TsmbagbMOatuyt/X78Zdr3yFUw+vxeKpJ6iO0yOkZRfsn97+Dn96+zvdcxhc68GqO8ZmVJZ8X5sfp8//D04b1hOPXDFKdZye+H/Mo7NP9+LK6pNEorwu0bRREh6g9Ha8erx3bpmXKVdp8EBsEdrb5k8/C0wohBjRbKpZKNY1HMSVCz/B7eOHYvppQ3W95rq/fYbPtzXj3TtPVWzR4AuqH49MDaCGNDPA2Gczg6TFF5QYQEqGRqo0eGnBx+TvarFwGFjjwTd72tDQ1GHYAOoIhBGOMn2NvvNPTzXoNn8I4/74nlA8VU5ViR3v/WKcrsKfiU2a8lhxLZtQJKq4eRTqAKUQQRsxgH75ytf4+5e78NbtP0GdwVCpXn79+jd4du02/HP6j3F47zLVcZloFAfXePDF9hYKgRH6C30JOhINy/zMI3ujqsQOp82i+w+I7UAzDZtt2t2GjkAY7/1vv2a9Dj3x/7OO6o1Dqty4YFQ/XZ/tsFlw6XGHYET/ShzZt8LYxHXgZt6ZPDRD9cuaoeaiWNj5I/ticI0HYwZVp/V65vaOxJtqmo2Nu1oRjvL4z3eNusaHI1F8sLkRrZ0hfLmjRXGMVmZUpiJ5ZgAZ1f8w1GrKKFUzTpUGz1KbHTaLqve1b0Ws6/z+NuPFENkGyGW36PZk6NHLfL2zFQe8QXAcFNe4Zl8I3+9vNzRHtV5l4t9FrSFqkNUBUvkNe5fHvP27mjt1zQkA3vtuP/yhKNY2HNT9GqO8991+BMNRfBmvqaZGJlmqF4/uj4E9SvCjIT3SmWLOMadZ1k2p9qTW7YQjCbe0WisMABh7WC3Wzz7T0Ocf8auV6AxF4A1E0CODFlosC6XNH8YBbxA1pc6kMTH3d+r4/4j+lVhz92mGPn/ehccYGm8EwThJUwOkLwQm9QCxG24uioXdNHYIbho7JO3Xi93e3kDYsG4l1zCjbIvOYn07mzsFQesPjR0YN6xn0hgtj5y8irdRhBYuaXovK0sc2N3qT/ImKGVcClW8VUoY6DnvMunPZVQALR6r9XlMlH36sJ54esrxkufOf2wNvtzZKhRhTcXBFL3KrBYOFi7W0kFNBxVKIYJmx3pHcyeCYWUvkpiOQFioc6b3vDZKKBLF9gMx6USqcGMmSRpXjhmAK8cMMD7BPEEeoDySSIdUP+HETRazHf9NtSDqRbz4qqXI+oIRwZDT0wnaLAg3uAw1QJqtMAQPkLQbvBmLhVksnJD6asZq0IH4cTrgDaJVx01afL6qnbtaafCJKt7GvWHRKI+GA6wGUHo7ELWaMkohsFRtTISCjxrrTCYZTKmMCyX06GUaNHRURvU2qWqVAVIhtBLBFCLonmVOlDisiER57NCh19wqOUcza0arxs7mTiE8mcq4zVWZDjNABlAeYRdZmz+MsMrFJN5ZOrMgVhbjyVJKszgNt0Flh3IwRXqpWXHacp8FJvcyad1wzUBCS2I+IbQ4LMGMCy226DGARGm/cpiBnI4GaHdrzANgt3LoV5VeAU+1mjLNCno7Qb+VQgOkdd6x1Hul2kOpMFIDSPg8HXWAtFqJGDXYEr+bupHGPDtqIbCECFpZH8dxibYQejw6es7RTBEbVql+K61Mwa4OGUB5RCJa7FS+wBPdxLUzrNLBk6UbmXh3pVYjJFV6qVmRt6kwip4QGOsontAAqdedMQOClsSE1aDFuiQ9u2XxmFQeIEUNkC2hiTKaGs4+r66HJ+0kBKUbfCwrTCkEFpur2vWuJfZmZNKgNJ0QmC4PkEYzWWaw6Z2vnkxVR4paQKnqAAHA4Fr9VbXFm8qtB3yIRLPfF01siKXylnlzmKVaaMgAyiM2qwXlrthio2Z1M5d+LrQWibYGmYUyJB4glQs6ncXPDLhs6Ytco1E+IYLWCCuIvUziVORSk1ZLNXNH+IAoVKnmjRQjNnr2tPoVW3xoufxdjsSSadQLlG4LDDFKGpnWzpDQqqBSIoLWbmPCssO0Q2Dp1xszWgMo9nnaHqBgOIodcTGxUhao0fnq8VKl6gifKgQGGOurJl5Tg+EodrfoF0/rRTyPVMZiRw6TNAoNGUB5JtUOhaVf5yJslGpHqBc9GqBU6aVmJZMQh9gboe0BSghpxTcnpeabZiBhAJlPAyQOS/yg5+YiM5KUzl+vhmfEYbWAOTSNGsnptsAQo+QhYf9f5rJJvBAlKY6bnhBYwgOUTggsfRF0RyCsGHLafjDmEfE4rKgtS06+ELLkdIqg9XiAUnWETyWCBkRVtQ2GwJT+nQ3E80gdAjN3iD4TyADKM6mE0P4ceoBKsrSTFxtvai7aFh2xdTOSSSsM8WtcGrtBcUdxZoxqpSIXGo8jO9qxXCAJgaW4ufiCYexu9QNI3JAUDaCAugaI47jEOWKwVtSWrHiAkj0kaoYG8ygGI1FFY0LL0Et8Xvo9B9PxAJW77YKB2dKZvEYK4a9aj2Jo3bgIOrUHKFVHeD0hML0eIJ7nhfNYOEcbsy+ENuIBIg0QkTVSuXgTGqAchMAc2TGAxDsGNRetnp2VGcmk0B17jd3KwaaxGLIbaDASRVun+XdX2coezAUSEXSTV7Mu1damWAZOZYkdowZUxV6jYDR1aGiAAHEWn9EQmLp4Vy9KomSW8i2/iYvnr3TN6xJBa7TfSEU6HiCrhUOlW32NTPUbGjHYguHEBkRrjo54yFpVBB2vA6SVtDIwbszsbw9oeuCbOoJoD4TBccDYw2sBZF8I7Q2EsbfNL/y7xRfUPLbptsLoCpABlGdS9brR0wYjXRI3ssxCGWzuzDOg5KJNJwPEDIi9M0YXfD0C6NjziWPLbmRmXlxKTa0BStyUOkMRoX6KEmLx7GCVTuchkbdEzTBIp19cIBxrJ8E+P12UNC5qmw2bNVEcUMl49erQAMnbbxhB0AGq9AJUo0rDS64lgAaMheyYh4njYp4nNRwpPEB6QmAVbjtqSmPfS8tTyb7fIVVuDItXZ852CGxrPFuSrd+hCK95TxA8hd1QA9T9vlE28XoBq8LiYLUCLpd0nBoWC+BOpLzWWkJwB/3oONia/DqLRdoGw+cD1G7CHAeUiJqH6hjLbrKBtg7tOXtEi0tnJxBNXPj+UASc1wc3gDH9q/HODh8aGjsw9rBawO8HIvHMpoOtcAf9qOXCic8Sv69orCIlJRB84YEAENZYfI2MdbtjxwQAgkEgJN0puoKx4wMAgWAYLqdddaz0hS7h2JVyUc3f1+VIaBcOtnTAHfSjmrcpv8bpBGzxyzQUis1DDfHYcDj2W6jhcAB2u66xpZbYedURiMSOmd+vOhZ2e+y9AWNjo9HYuWZ0rLdDOF4AsHX7PvQ+rHfstwBi14Qv5vnZsWM/3EE/hpVaMbSEgzvox/Y9zYnP4Hn4DrYJ71cSCgBiLUn8umcGbLC1DahWuXHK1ogd2xvhCvhR5rShhgsBXtE5KlsjtM6dai72OiFs4fOh40AL3EE/elnC0tdyHDxOGwLhYMzYka0RwdZ2uIN+VESDsefE60n8uncB6IEQfMEIWhubUcEqWGusEcLDzbHfUmKYpbruPR7BiGltbge8LsnTO3c2wR30Y6iHi30X2XVfjdj1G2gJgu/oSITJFNaI1sb49y+xw9opqs8jWyPKIgG4g35E2zsAr8zz5HYnRNDRsOaxG1LtRlNHEFuaOnB0T7fierI9fo4Oqa4WMse271O4V4gxuEYwI2t4zxJs3n4AgXAULfubUSpvRh1fI7yBMKzRCMoiAfV5iNcTM6wReuGJJFpbW3kAfGvsEkv+mzhR+oKSEuVxAM+PHSsZ6quoUh973HH8C+u28XV3v85fs2gdz9fVqY8dPlw6h+HD1cfW1fE8z/NPvPc9X3f36/y2wRpja2qk7zt2rOrYgNPN1939Oj/7ta9jYydOVH9f+al28cXaYzs6EmOnTNEeu39/YuzNN2uPbWhIjL3zTs2xrZ+uT4ydM0f7fdet4z/bepCvu/t1/vGJN2iPffdd/rB73+Tr7n6dX3P7fdpjX389MYdFi7THvvhiYuyLL2qPXbQoMfb11zXHvn3Lr/i6u1/nZ736Nc+/+672+z7wQOJ9163THjtnTmLsxo3aY++8MzG2oUF77M03J8bu36859rUR4/loNBob29Gh/b4XXxw7zR9+n6+7W/s3k68RIbdbfaxsjeBralTHho4dzdfd/Tpfd/frfGcwnHKN+PEfVvN1d7/Of7b1oK41QuC449THGlgjvHYnv6VRdC3rWCOuWbQutk6dlmKsCdYIfuNG4VpuvesezbGPzFvG1939Ov+nVf+LXScaYxf95hm+sd3P1939Ov+rM27SnoPBNeKRt7/j6+5+nV9yh/Yc2Box5rdv81dfPEd77GOPJeZQ4DVCuH+3tvKpoBBYnklVE4elUedCA8RCYFGez8r7sVImuchSMAMBgxoPFgKzW1JfVuz4mlFXI0crjNLVCUV4w0X+0snQzNIlB6uFE2oI6dG5eLKk+8sEo2Fw5jFSq7xsNlgILFVtp77x4pd6ND19Kt3o4XGgzGVDlk4dAbZe9yp3pRgZw4yh72zB8byxS3PgwIG45pprcPXVV2PAAPP2+MiEtrY2VFRUoHX3bpSXlycPyCAE9q9PvseMF7/EcQOrsPTaMUljn1y3G7//17e46NhDMP+coeorZxohsFfX78Qdy7/EaXWleGbycepz1nBvf/xDE65Z/BkOrfXg/vOPxIV/+wr9Kt348JenSdzbZ/zpPew86Mdz15+AUQOqk9/XpCEwADjhN2+jPRDGm788E4N6lmmOFXC5sPq7Jly75DOM7l2CV64/QXPsiX94D3vb/Lh8ZC/8fd021B/ZCw9dPip5rAlCYMvW78G9r/8P9Uf2wlNXjiq8e1s09rxH12Dz/g5MOLI3Vv53L8YeXosnrx6TFALjeR4nzXsHrZ0hvHrLjzCsdzlOn/8edrSF8Nz0n+C4gdUAz+PL/+3C5X9Zi35VLrw941TpHOLX/U+fXos13zfh0fMOw7kj+irPV7ZG3PvsJ1jxxS5MH3cobjntUOlYAyEwWCwY/eAaHPAG8a/bTsERFTbc+twXWLVpH351zhG4ckxdYizH4eIlG/DZtmY8+dNjMWFwhWSNuHLhJ1i/vQUPXzYSZx7dRzEEBgDXLv4UH/1wAH+4+GicNyLetDhFCGxfmx+n/vE9WDjg6z9eCAszDnSEwH7z+iY8vaYB007sh7vPGCo8tXFXKy558mP0KHXE+geqXPdj//gO9rcF8dJNJ+GofhWx5xXGvvjZDsz5+39x6uG1eOKnoxNzkK0RtyxZi3e+2Y/7zz8Slx7XXzLdsMOJQ3/1FgBgwy/HotKubgS99UMLbnxuA445pAL/uOEExfXknEc+wA+NXvzlhpNxyrDeOP+xNfjvtgN44pKjcMaRvZXf2OAacf5Ta/HljhY8dfkxeP6D77G24SD+eMkxOOcY2bnscIC32TDknjfBRSL46I6T1Y0mE4XAhPt3a6vy/VuEYQ3Q7bffjsWLF2Pu3LkYN24crr32WkyaNAlOZ3JNhi6PxyO90LXG6aS8phKdDhf2RayKr5OIoEtKkp5XRcdYVsiqmbfpn7NbWrL/AFrR6XDBXVWBugGxRpK7WzvhD0XgEi34e0I2dDpcKK+pUv4sl77dB4DYBa73/DIy1uFQjBlHPR508gF0hvmUY8UwDZDV7Ur5+zIdSaOfR6fDBXt5WepjYrcnFplU2GyJRTHDsSWe2LHyBiKxG7vec8fIWIslrbHtNic6HWEMGtgTnZtb8L/2qPT4cxzg8aDZG8TeiBVwWFHXvyfgsKJP3xr84GvCliZvzADiOLRbXeh0uGArUz8ezHvXYXPqnvN3HVF0OlzoP6A29WtSPF9ZYscBbzAm9O1Tjn0RKzodLpT2qEx6LSt90RGIJK0RB2FHp8MFV1V58vohuu7dVeXodHjRGLUrz82d3NajuT2CTocL1R5HwvgBdF33LNOtKcRJPu8HXws6HS707VudPA/Rde+urECnvx0HoTLf+NgmPrZGlVRVqP/mDgdQ4kGnw4VOe/J1HRKJh+1uF6CRUTe4V8xIbGj0grfbwcnWk0iUx/86ogg5XBjUK3bjHlTjwZc7W7HZx+MMPedaijWC53khrX5g70qUVJejc5cPTVHle4I/GEGUB2CxwlNdofn9BMywRujEcAjs9ttvx4YNG7Bu3TocccQR+PnPf44+ffpg+vTp+OKLL7I6ue4IE/ipdSvu1NFMM12ykc0jru1R7XGg3GUDzwPbDiREhOL00uoulgYPJLeq0ItQBVrHsWM30aaOmOfFzDU2hDCKCUNggfj1wjJmth/0KWbrsPTpvhUuoRClUiaYngahzHg1UisqG1WgGfJUb62My1KN/n966gBJP09/qJAVIkynEKpa+40tGk1QlV+vPV+9mapadYDEqfGp6ngN6FECCwe0B8Jo7Ej2uO5q7kQowsNps6BvRcyoZOn+eqqc6+GgN4g2fyzNvq5HiahukvL9SJyyX9KFejrqJW0N0LHHHotHHnkEu3fvxpw5c/D000/j+OOPx8iRI/HMM8/AYGStaBAvJkq/USAPGqBMKvq2eBO1PTiOwyCFHjd600vNitDx22C5AD2NUIXPEAwgSoPPBKYT6V9dArc93nH7YHLHbXbzZJk1gKg4nejmoqfztdtgrajWzpBwnAdmwQCSF/vTajujZbwmCtxpn3tVaVSDTqcGUOLzlA0ucRFELao9+moBsTR75nFSQ6sbvPgxu0ozVIbTZsUhVTFPm5JBs0WoceQRvGZq5RrShb1P3wo3XHZryuaxXtGGwJJm/zozk7YBFAqF8OKLL+K8887D//3f/+G4447D008/jYsuugj33HMPrrrqqmzOs9vALu5wlFcsiMVqi2j1kkqXbDS1FDxA8doeQol30QXKFp4Ktz3tpo+FJN12GMxg0uMBYmMOeLuAB8jErTDEGwatjttK9WOE8SLj3Sc0flQ/HuJaUXpoEESnzqwUvBQXU+V5XrjelLwtWn3chIKPKeq7pPISKKHUnV4vah4cvV40vdWg9Vaq1uoGn+gEr695tVZFaCUP1yCF9TUTEhuB2Psq9ZYT06FjQ9CVMfytvvjiCyxatAjPP/88LBYLJk+ejD/96U8YNmyYMGbSpEk4/vjjszrR7oLbYYXTZonVXvCFUOaSXnws7KJVVTRdSrKQEcJ2Ciy0pdTjptmb/u7PDAgeIIMd4VlIRI/xmgijxD7DzEXGstVDLhewQohOmwWDaz3YtKdN8eaiZAANjocXWDsXq4UTNQjV8ACJernpoUG0s88GQj9BbxC+YETwQlQreDI8QghMOlc9BR8Tn6ftJVAik0rwStWceZ5PGEApPECpqu0z9Hqp7EI3ePUQWCrvD2NQjQf/+a5R9znK/v+gN4gWXzDjyvrydiypCvOyxtlmrlSfCYbvsscffzw2b96MJ554Art27cKDDz4oMX4AYNCgQbj88suzNsnuhtZJl0sPELPiQxHecIo3Q+5uH6Tgok2nB5CZcKXpAWLjWbd3LeTH18w7LDY3ta7ihYLneeHm77BZFL2RDKXwSb8qNxxWi6SdS6I9hIYGyGZMA9Qg7OzTb4EhRrxrZ9ejw2ZR9Dx6BBG09Nj5RAZRqnMv0b9QvwcoYVwYXwOEEJao/UZjR6yFhIWLhTu1SHVTZ+ht2Mw2o5oeIJ0bVma8aZ6jIgPI47ShV7lT8nwmyI3xhHGrfGy7cxsMIA0P0JYtW1BXV6c5xuPxYNGiRWlPqrtTWWLH3ja/otvRH7/IXDpuokbxiG66vkBE141ajty4UXLpZhL/NwPuNESusfFxEbQeD5Dst9e64RYaueGcznmTC8RtMJw2i8gYlzaPjEZ5xfCJ1cKhrkcJNu/vQEOTF/2rS3S5/AUDWadGLBtNUMWINTnNoj5gSiGYUhXjtSP+b7uVS3nzTksELawTxtcAefuNCrdd8DAfUlWS8vzTG7LT27A54QFK1mzqaYQqJhGmTW5wqubhGlTjwb62ABqavEIPu3RJbARixniqcGEiKcC8G7RMMOwB2r9/P9auXZv0+Nq1a/HZZ59lZVLdHa0FxZ9DD5C4N1C64QzBuInv0gb2kLpogW7gAUqzI7wggtZhILi6kAdInP1hJh2Q1ACyJjJmZDvl3a2dCISjsFs59KuUpmzLDXifjsyoRDNUYxqgbIXAxDctLQE0kLhxdciOmx6xN6M6DQ1QJpsgp80qZOGx9zHyGwoGokaBS57n0dKpzwDSFEGHjXmA2Py3H/QhLHo/fyiCXS2sV1yp7DXK57VRIlEeW+PZuvIQmJoHSI8mritj2AC65ZZbsGPHjqTHd+3ahVtuuSUrk+ruMLej0gWayzR4QJTRk2Y446BX6tr2OG3oHS+OxS7Qru8BSs8ACggaoNSXlfz4mnmHZbNaBM2SmTLBpCnIHAbFjfF9bdKO2+y8HFBdAptsp868RmxHLmRGaabB6/cAibUrqbKX9CLWuKQK46ilwXt1CqCBRMJDZyii+5rIRAQde53U6DJiAOkRQbf5w4hE+fj4TETQsfdw6PQA9a1ww2mzIBThBYMHSHy/Crc96TcbohE2M8Lulk4Ew1E4bBb0jW8E2Gd1BMKK369DyBQ07/qUCYYNoE2bNuHYY49NenzUqFHYtGlTVibV3dFy0bIFxpmDbvCAdlZIKsKRqNARWuzalu+imzOI/5sBl8E0Z0anzm7wsTHS42v2HVamhnMuCIgSBjiOQ0WJHT3insmtoptFIrSQrMGR64b0hMCMGMj72gLwBSOwWjgMSKFd0YsggvYFU4Zx1K53ZujpOe/KnDbYDLTfYHMD0guBxV4nzQRjIbAhOoxIPSJotklz260pr1etOkB6OsGLsVg4xcwucfhLHsrUym40Avu8gT1KhOzccpddaGnEypeISRjK5gh7ZxvDd1mn04l9+/YlPb5nzx7Y9FaeLXK0ai/k2gOUSUpza2diQakU1fcRhH2NzABKP/5vBoymOTP8Bgwg+fE1u8gwE8M5V4gzwBhKIlMh9VfBeyAPL+hLg9evEWMp9gOqS3TfJFMhdEvvDOGAV9vQKFGpAyRoO3ScdxzH6S4uyEjU2MnQA+RlITAm3k0tJGevVfNqAIk1SilzTg47v7SywPSGwADl+lNaHi722NYmL6LR9OvrsQrQ4s+wWDhUuFlEItlgNBIq7YoYviLPPPNMzJw5E62trcJjLS0tuOeee3DGGWdkdXLdFa3aC7lshgokLPl0bmRsvmUumySUkOQB6upp8PEbXG49QDIDyMQhMEBdS1JIEjefxG+Z7s1lV0usnYsel78RD2G29T8AUOmOXVc8D2w/EHt/NW9rqcqGx6i2o1JmkGgRjfLCZindNUBcDTociWJ7vLilnjBiudsutP1S8moAie+hR6eoFQJLiKD11ztTShzRMtL7V8c8Np2hCPa1a/TNSkHiXJQakVpZc3qrhXdVDBtADz74IHbs2IG6ujqMGzcO48aNw6BBg7B3717Mnz8/F3PsdmidcP48eYDSEUGraXvku+6uHgJLVwOUTisMhtkXGKYl8ZncA5Tw6CSybLZo1OGpKY133OZjwlQ9ab9GRPINOts3GMFhswiGC7upqYfAlGs4GdEAxd5fuT2FEm3+EJijIt1ECHGiyE5Ri4g+OjqYW0VeDbUwWCrxuBjBAFLKAgsbC4EBygU4tTxcdqtFCJ9m0hJDLRuxUiMikQiVmttDnS6GDaB+/frhq6++wgMPPIDhw4dj9OjRePjhh/H111+jf//+qd+A0Ky9YCSMkg6JtFjjO3k1YSO7aJmLtqXbhMCMGkDGW2EAMQGlERd6IcjEcM4VQUUDSGqMB8IR7GyOZ9coeA84jpMU8zTSCkNPiHRLDjxAQOKmxTwH6iLo2PcIhqOSEI5Q8FHnjU1vdeXYmNj173FY0y6ZIE71F3vR9LZjqE7hsTKSqcquzVAW6gABovYWImMm1XmSjYrQamJ8rYhEd0+DT+tbeTwe3HDDDdmeS9HAFpODsoszFIkiHN865c4DlH5VXzVh4yFVbtjiLtq9bX4hvVRPfN2MuA1k+Ygxot8SjzG7/gcQ9ZQykQHERNAOBQ1QQ6MXPM9j+wEfeD5mCNSWOhXfh3Xc3tLUkXD5ayz4hjxAWa4BxKgqcWBncyfa48cjVRo8EKv9VVEizebTGwJL1TNKTKYCaPFrm32htIxItYaqDCOZqgkPkHYrDL2wDePuVj86g7HMOrZpHFijLJQfXOPBO0hfCC1Ns5cZQB5147a7p8Gn/a02bdqE7du3IxiU/mjnnXdexpPq7qjVARIvqLnKAsukHYZadVfmot3S5MVXO1t0p5eaFaesTYVejHjvxF6irrC7EloqpOE5zBWsD5hT9HsPqC4BF++43dQRTLj9FbJrGOyG9P2+jkRrkiw0Qw2JtCtKGWiZIL+21MTGDpslVu06EkVHMIyK+OuMajtS9YwSk6gVlv71L26/wcJDqVpgSF6fonijkTC9w5a6G7yREFi1x4HKEjtafCFsPeAVvPF9K1yqa4FakU+9bItvBMpdNiFTkqGVNdfd0+DTqgQ9adIkfP311+A4TihVzhaXSMQ8C6RZqRIWoYhQlwFILKgcl5teYEBmnb2FEJiCZ2dwrQdbmrz4YnsLAH3ppWbFaLdvBvMYGc0C6wq7KzNmgbEduVN083HZrTikyo0dBzvR0OTVJUJmN5eNuxOJHdoaoEQWGM/zqobVjoOxHmNuu1VoZ5At5N5VLU+Gx2lF0BeV6LeMeoAMhcCE6tTpe4ASWWAh8LzxViKpqkEbyVR1WGPngrIIOl4HyOB6PajGg/XbW9DQlDCAtATeWk1U9SBojGpLk85XLYF7d2+FYfgue9ttt2HQoEHYv38/SkpK8N///hfvv/8+jjvuOLz33ntpTeLxxx/HwIED4XK5MGbMGKxbt051bCgUwty5czFkyBC4XC6MGDECK1eulIwZOHAgOI5L+jNLoUZJ7QXRgiJ0trZZdXUWTgfhRpbGTl7Lbcwu0M+3NcfHdE3vD5CBBoi1MdHhvXN2sRBYJoZzrhDqAMl+b7EQWo8ImYWnvt8fu0nYrZymdoVV8Y7yymERhtj4yvb1LL8GtQ2gZP2WV2j6qu/c09tgFMhOCEzswUknky5VyM6Il4pleGmFwIyWOBAbNHqa5bLGvTuaO1VT+7XQasei5d0zKpbvahg2gD7++GPMnTsXNTU1sFgssFgs+PGPf4x58+bh1ltvNTyB5cuXY8aMGZgzZw6++OILjBgxAvX19di/f7/i+FmzZuGpp57Co48+ik2bNuGmm27CpEmTsH79emHMp59+ij179gh/q1atAgBccsklhueXCyS1F0QnnaAhyWHRKY9KZVg9iPsOyWE3na93xXbRXVUADRjv9g3Eysyzhcm4Bsj8i4sZ0+CFEJhs9y0ubqiVAcZgz7HMpVTHQ9zqRCtMyvQa2aoALUYcAuO4WOq3Ggn9VuLY6Sn4KP08/R6glgyrQMdeG/u8po4g9rTGUr+N6KiYl1qus2SwtUyXByjLdYCAxHf5obFDNT1dTK9yJ9x2KyJRHjuafYY+C9DORtQyFqkOkIxIJIKysjIAQE1NDXbv3g0AqKurw//+9z/DE1iwYAGuv/56TJ06FcOHD8eTTz6JkpISPPPMM4rjly5dinvuuQcTJ07E4MGDMW3aNEycOFGSgl9bW4vevXsLf6+//jqGDBmCsWPHGp5frlBKhRc6wecwdORxJO8G9XJQY2fHLiy2IGQS/y80RrJ8GMwbAegzYMVjusLuSq2lQiEJqmTgiCvnJkTI6jcXccdtIPXxsFs5oZKulpGc7SaoYsQenwq3XZiPEkqJD0bFrfn2ALH2G+wYV5bYFUPvqq/PhQhaKwvMQB0gQFqAU6sGEIPjEhWk00mF1/KiqRm3PM8LkYKuEKZPB8MG0FFHHYUvv/wSADBmzBg88MAD+PDDDzF37lwMHjzY0HsFg0F8/vnnGD9+fGJCFgvGjx+Pjz/+WPE1gUAALpe0FoTb7caaNWtUP+PZZ5/FNddco+qGDgQCaGtrk/zlGqXaC7lugwFkpuXQWjTkAsWu7AFKpxCiOGNMVzNU0THuCrurROjUPAZQwgMk/b3ZIv/VzhY0dcTOWbXsGvlrgNQhSY7jdNWK0hPaSBexByjVTdwjlL5IHDuj4tZqjUwhOdnwAInbbwDGf8PUImj9c0x4gDLvBs9QMtJTfcdEvTXjQmhxMoActbIsgXBUSGjRWy6hq2H4Tjtr1ixEo7GDPnfuXDQ0NOCUU07Bm2++iUceecTQezU1NSESiaBXr16Sx3v16oW9e/cqvqa+vh4LFizA5s2bEY1GsWrVKqxYsQJ79uxRHP/aa6+hpaUFV199teo85s2bh4qKCuEvH/WMlOKuuW6DAWSnDpBSdlfPMqekX0yxaYD8Ile4nlolUhG0+RcXM4qgxb3AxLAbyb62AIDYuVnm0j4fxeEHPUaBHiNZqwdZpoiNnlTZlkolDIz2eGIbmtbOkHBTVONgFirBi9tvANoePCW0Wnf4QxHhuOkTQavXAUo3BMbO0dbOEALhKOxWDodUuTVfMzhNIXSLLygck4E91DVALZ0hSasNscewK3ip08GwAVRfX48LL7wQAHDooYfi22+/RVNTE/bv34/TTjst6xOU8/DDD2Po0KEYNmwYHA4Hpk+fjqlTp8JiUf4qf/3rX3HWWWehb9++qu/JWnuwP6Vu99lGye2Y6zYYQMKSNxoC43leJBxMXjQ4jpNoHbpqGwwg8fsHwlHdvXeEDDCdC6H4GJd0BQ+Qgo6k0KjdfPpVuiWP6fEeiJts6lnsU3WE9wbCggE2SOGmkyni60uvB0is3/IZTINnBgXPS3sCKpGqQ71exMaJkRR4QOwBSp4re8xq4VDuSv397fFzKZBFEbTbYUXfikQkY0B1iaS9kBKDZD0X9cIMpt7lLsXjzY5TJMqj3S8KkwYSG3KtEGtXxtBRC4VCsNls2Lhxo+Tx6urqtLIcampqYLVak5qr7tu3D71791Z8TW1tLV577TV4vV5s27YN3377LUpLSxXDb9u2bcPbb7+N6667TnMeTqcT5eXlkr9cU63gdsx1Gwwg/WwebzAiuIDVvDviXXRXDoGJf/+AzowLv0EBu9hr0RXi64k6QGbyACmLoC0WTmJ06Ll5GgmBAakb5rKbTg+PQ6i9k03ExkUqQ0NoYyIJgRnTANmtFpTFx6YKg6XqUK8X8TqTdgisMySUamEkPFR2XfctcTd4+Xul6wECpOJ4PSn+8sa9ekkVYnParEI2oPjYGhXKd0UMHTW73Y4BAwZkrdaPw+HA6NGjsXr1auGxaDSK1atX46STTtJ8rcvlQr9+/RAOh/HKK6/g/PPPTxqzaNEi9OzZE2effXZW5ptNlGovGGmlkC7ppsGzeTpsFlUDTXyBdYcQGKBfB2S0hQnHccJx1huGKCTmTINXv/mIz0U9N0+pAZR6wU+lAcpVCwyG2Aur3wMUO3bhSFQw3PSmwQMJYXKqatDsJpppJXjxJsro7yj2arT5pedsi0GRtjNeB4jnkRT+Y5tCI5WgGeLvZMRI398eMOTBV2uBIUYpKSdRLNP861O6GD5q9957L+655x4cPHgwKxOYMWMGFi5ciCVLluCbb77BtGnT4PV6MXXqVADA5MmTMXPmTGH82rVrsWLFCmzZsgUffPABJkyYgGg0irvuukvyvtFoFIsWLcKUKVNgs5nPgtXSAOUyBFbqUO4NlAqxsFFt1zS4pnuEwKwWTljQ9BpA6ei32NiusMMqcZovBJbwACX/5kZ316zjNqDPKyIuhqhELpqgivE4rIJnItVmQ67fEm9+jJx74uKEanQGI8JxyTQEJv5eStoVLVz2hFdDbrAZEUADgN2WWO/ktYDS6QbPEJ+Xes6TCrcdNaWxY2AkE0xPlpmSELq71wAC0qgE/dhjj+H7779H3759UVdXB49H+qN+8cUXht7vsssuQ2NjI2bPno29e/di5MiRWLlypSCM3r59u0Tf4/f7MWvWLGzZsgWlpaWYOHEili5disrKSsn7vv3229i+fTuuueYao18xL1QpiPTyqQECYie43l2Qnu7J4ou4q7bBYDjtsfYB2w54k9zeSuyO99lxGjh2seMc6hIGkGA4R6KS6uWp6AxGclbXSk0EDRj3ALF2Lg1NXl2tSQQNkJoBJFTezY0BFBMJO9DYHkh5DTMPIzNeWSjMZuEMVZzXUwuIPWezcBmHdtla07fCldY5VFXigC/YiWZfCHU9kueod+0T63tCYR4QvUxohZFGCGywwXOUjYu1eOnA0YdU6HqNVgYYQ9EDJHSCN//6lC6Gv9kFF1yQ9UlMnz4d06dPV3xOXl167Nix2LRpU8r3PPPMM3XduAqF0mKSjywwe7zzeDAcRUcaBpCWYdNdRNBA7Bi0+8O4cuFaQ6/TK4JmnwF0DQ+QR2Y4O2ypj+97/9uPa5d8htnnDMeUHw3M+py09Bfs5mK1cBhQrZ0CzxhU40FDk1dXVp5eDZDR7CUjVMcNIL0hMBbSEBe3M6Ld1FMLSGxcZFr9moX50s2iqyyxY1dLZ5LBptbTUA2bhQPHxUJgcg9QOs1QGWKDRK/Ie1CNB59ubdatA+J5Hlt1FFpUah3S3dtgAGkYQHPmzMnFPIoOJZdjIA8aICBm0R8MBw2lwusRNpa77DhvRF/sbfOjv86bjlm5YFQ//O3jrTBiQ9utFpxzTB/d488d0Rf/2rgHI/tXGp9gnrFZLShxWOELRtDmD+kqSvf5tmZEojzWb2/OiQGkFQI7ql8FRvSvxBG9y3R7qy48th++39+BnxxWm3KsVr84nudzrgECgPNH9cXLn+/ECYOqNcfJQ2BCDSCDXhUlL4Gcnc0xT2jvisx7n409rBbPrd2OC4/tl9brqxR0loA4BKZvk8ZxHOzW2KZR1QBKwwPUv6oEpwytgctuRW2pvt+rT0UsVX5/e0DX+PZAWDhH+1S4VMcpVYNmOqOukKWaLt33m5kccaGuaJSHxcLlxQMExISPB73GUuGbNVLgxTxyxaiM5mYW7pl4BO6ZeEROP+OOMw7DHWccltPPyCZqIQU1WLaNkYraRlDLAgNiHpq/33Kyofc755i+OOcY9XIZ0vdX1wA1dQTR7g+D44C6HrnbCNx86qG4+dRDU46Tp8Gn294gVXVlILueryP6lOP9u8al/Xq1+aZTqdoRN4DktYDS6QbPsFg4LL12jKHXpOpxJqclrtdK1ZxaKSIhVAvvxhogw0fNYrHAarWq/hH6YBdnlIdQe0GoJZPjrKB0MnqavcbcxkT3Q6u4nBLMa2ikorYRgnENUDq770zRygJjRkC/SndO9Xx6kafBp2sApaquDORe/G0EtfmmU6larR9YMIMssHRgG1AtIbqYZp3hvioFY9FotfCuiOFv9uqrr0r+HQqFsH79eixZsgT3339/1ibW3WG1F3zBCJp9QVSU2BPdxHW0UsiEdKr6GnUbE90PPTdAMWzxNVJR2whaHqBco1UtPJctMNIhOQvMWA0ghh4DmLVpMFq4MBcoJZqI/23EA8SyvOR1wUIZiKDTwUhTWvG4VN9V6domDZACSvV2Lr74Yhx55JFYvnw5rr322qxMrBhIhBSCGAhPohlqjj1AnjRSmrPR4JDo2rAb4EHdu8/YuJwZQKH09ReZopUFlssmqOkgb4DMdvZGagABon5gGsc/H+JvvSgJe4Fse4DSF0Gng5pRp0ZCuqD9XQXjVnRsvQarhXdFsnbUTjzxRElBQyI1lbKsCpbWm2sRtJAWa6CqbzYaHBJdG6MeoBZfbjVA7OajJILONWyTovTdWBgoFz3A0oHdwPyhKMKRKHwGq0AzUomgWztDupvP5oMqlcKNevWMYtQ6widE0PlpFSGuH6cny5kZNJl5gMgA0qSzsxOPPPII+vVLT61frMg7LAseoBzrBuSVYfVAHiAi3d1nrjRAQh2gHG8YlGDlDrQ8QOYJgSXWE18okrEIukXl5svSrWt1NJ/NB4lq+wmvRiTKC73MjITzhYaoEeVWGOmIoNOBHYNgOKrrutKb8s9+i4OKdYAoBCZQVVUlqe/A8zza29tRUlKCZ599NquT6+7IXbR+YUHPjwjaZyAERh4gQi2koIQ/FBG8I7kKgQl1gPJ08xEjeIBkpSQiUR7bDpjLAHLaYlWjQxEe3kA4EQIzeGNjN8lgJApfMJJkQKXqOZVvlLwabZ0hobSFkWKtaiGwTNLg06HUaYPNwiEc5dHsC6Us2qlXu8nanPhDUfhDEbjs1kQafDfOAjP8zf70pz9JDCCLxYLa2lqMGTMGVVVVWZ1cd0ee0pg/D5CxjvCsaCJAIuhihnks9YTAlAp8ZhsmSM11yFgJQQQdln63Xc2dCEV4OGwW9K10531eapQ4bGjtDMEbCAseIKPpzSUOaywdPBJFsy+YZACZTftUrWCwMw9HmdNmyGvDxspF0Pn2AHEchypPrABmszeIfinOMb2e+zKJYRVEnwp3Ig2+G4fADH+zq6++OgfTKE7kiv58tMIAEha93iywls7Y/DgOKHeTB6hYURJKqiEek2sRdCE0QIIIWuYBYllQA3skeouZgVInM4AiaYtbY+037NjfHkCLL4RDZPvdBh0tF/IJ82p0hiKCV0NohJpCFCxH3BFeTL5F0EBs49wYPwapYGOqU3xf1lqlqSOAZm8IfSrcQgiMNEAiFi1ahJdeeinp8ZdeeglLlizJyqSKBXntBX+eCiGWykrjp4JdRBVuu6kWdSK/GBFBi8eEIjzCBhrv6iWY5/CDGLVWGGYLAzGY11fiAUrjxqYlhN7SyNL/zSH+Zl4NILGGMcPcqCfbETeyk0Ng8TpAeTwHjaTCG9FuyiMSHcJ50n01QIaP2rx581BTU5P0eM+ePfG73/0uK5MqFuSl2v15aoVhNA0+UQSRwl/FTJUBDdBB2eLsD2fXAApHoohEYzefQtQBUiuEmPCCmMMIYJSIUuG9aWqAAPXqyjzPm874Yx4rIFGVPN1kDkfcAyTOAotEeeEczFcIDDBWDVpPC6PE+0ozzLxFoAEyfNS2b9+OQYMGJT1eV1eH7du3Z2VSxYJ4MeF5Pm+tMBLdofV5gNhi19U7vBOZIQ8paCG/QWY7DCbWYhQmBKbcCmOLiSohixF7fTOp76LmBdzfHoAvGIGFg+7ms/mgUjbfdJM57NZkEbT4/1mILB8Y2YjorQQNSAtdBiNRhOPGHYXARPTs2RNfffVV0uNffvklevTQ0SCIEBAvJsFIFPHzLeetMIymwbOLqJo8QEWNUkhBjRZZA0q5ViZTxDvxQrbCkAu8G0wmBGYkQmCRzEJgHmUdGDP8+leXFOR4qCGXGSQMAqMhsHgdIFEavLgxqhlDYIFwRGh4rS8ElrgfiaMDRpvmdiUMH7UrrrgCt956K959911EIhFEIhG88847uO2223D55ZfnYo7dFnE8XawlyFsrDJ0aIKoBRADSkEKqxVe+Ow2Ec+MBslm4gujSlDRA/lAEu1pi3dDN5gESt8NIdIM3bgCp3XzNavjJ55uuN1upEKK4MardUogQWIpNSPx5q4VDuSv1sWYe3mZfSDCSnTYLbAUoM5EvDF8Bv/71r7F161acfvrpsNliL49Go5g8eTJpgAwirr3AXLRWC5dzd6rROkBUA4hgxDJFgikNIHmIpDOYXQ1QoICNUAFpKwye58FxHLbG6/+Uu2xCyQCz4BFlfmbS40mtGGai/5m5tE9yvUxLhh4gcdiLeYDsVg6WPBrhQtFCr/Y1yJ6vdNslpWtSvW+zL5h2v7iuhuFv53A4sHz5cvzmN7/Bhg0b4Ha7cfTRR6Ouri4X8+vWiGsv7Gn1A4hVmNVzsmYC6wGkOwTGRNAmW9SJ/FOlMxVefoOU18vJlGABG6EC0kSFQDgKl92a6IReW5rza9gozAPU5g8LYbt0tB1qxTAF7ZNJUuAZcr1MwpttbDPnUPQA5V8ADYgrcqfywhr7rmLPUjG0wQDSMIAYQ4cOxdChQ7M5l6KDhRSaOoLYGzeAct0IFUhY9YFwrDdQKhcniaAJhl79gfwGmW0NUKITfGH0CeJaXazGDCsEOMRkYSAgkcrc1BEQPWZ8+a9WEUF3lRCYkawoMUp1gIKRSPy5/BpAVR59Imij37VS5FnqKIIaQEAaGqCLLroIf/jDH5Ief+CBB3DJJZdkZVLFBDs5d7fGtAP5WNDFJ7VXx40pXbcx0f3Qm4LLnmeOkFxlgRUqBGa3WoSbIvOomDUDDEikMu9vixlAFi4975kggpbUeYpi+0EfAPN9d7leRkjoMOjNToigRQZQOP81gIDUTWkZRrWbUhF03APUjQXQQBoG0Pvvv4+JEycmPX7WWWfh/fffz8qkigl20uXTA+SwWQSXrp5U+HTdxkT3Q+/ukz1fW+oEkP12GEIj1AJmHLFkBSaEFnQwJgsDAQlvz7722DrjcdrSCtMJaeWiEOjO5k6Eozxcdgt6l7uyMNvsUSVqOM3zfHZF0AWoAg0kjLp2f1izwKhR7Wa1ggiaPEAyOjo64HAkW5R2ux1tbW1ZmVQxwS7E3S1xDVCe+hqVOPXXAkrXbUx0P/TsPsORKNr8sXOmT7xXUa48QIXoBM9g5SpYeM9shQDFsBvZvrbYOpOuuJUd//ZAWDAAxALofIqB9ZDwaoTgC0YEA8Z4CExbBJ1PKkTtiFo61TciRrWbzLht84fQ5i8OEbTh1ePoo4/G8uXLkx5/4YUXMHz48KxMqpgQPEBtsRBYrosgMjwOfbWAeJ4XLjIygAg9Kbitoo7bvctjHiB5y4hMKWQneIZQDDEcQbM3KHgXBvYwowEk9Valu7OvcNuFsCY7B1joz2z6HyBxvh70JjIXHVaLkAiiF6eQBZaoAxQqUBjWZrUIae1aoWi9neAZlXHDiueBPfFyDulkCnYlDF8Fv/rVr3DhhRfihx9+wGmnnQYAWL16NZ577jm8/PLLWZ9gd4elwu8RPED5OeGEVPgUGqA2f1go904hMEKPCJotvGUuG0qdierR2aTQImhA1A4jGEFDPAW+d7nLlGED+ZzS1XbEasrY0doZQosviNoyp6k9X2KvxoGORCjfaPhPKQSW8ADl3wiv8jjQ5g9rhqIT2k1967bNakGZy4Z2fxg7m2MGUHdugwGkYQCde+65eO211/C73/0OL7/8MtxuN0aMGIF33nkH1dXVuZhjt4ZZ5wfi7sp8GUAsBJbKA8QuIrfdmre5EeZFHFJQQyyaV2sZkSmB+PsVNATGDKBwBLvjGj6zdEKXIy96mImRVlUSM4CaZR4gcxpACa/GtrhQOx1PtmAASUTQhTOAKksc2HbAJ4S5lEingG1ViSNmALXEfisKgSlw9tln48MPP4TX68WWLVtw6aWX4s4778SIESOyPb9uj9w6z1cIrFRUGVaLg15juwiie6NWCE9Ms0h8qdYyIlOCBRKgihGKIQajIh2M+YwAIPlGlokBJPcCCh4gExp/dqsFZfHvyuo0pePJViqEWIhO8Aw9oeh0CtiysbuaWQiMDCBF3n//fUyZMgV9+/bF/Pnzcdppp+GTTz7J5tyKArl1ni8RtLgyrBYtQtYE6X+IxHnQ2hkSQqNyxDtPZiQEsqwBYu/nLKBX0iXqCG/mMBCQrOXIJL1ZXArBGwhjb1xYbUYNEJCQGTAjNT0PUHI3+EJlgQH6khGEvmcGUv7lhS5JAyRi7969WLx4Mf7617+ira0Nl156KQKBAF577TUSQKeJ/GLMmwha6AeWqqt3enUziO6JOKTQ1hlSXFzF2gO3LFMqWwRMIIJ2xzcrnaFIQghsQi8IoKAByigExgrmhYT2H9Ueh2k3SVUlDuw42CkYqayWkRGcSq0wCliLKtGTT9kDFInyaO00nvIv9xal0y+uK6H7yJ177rk4/PDD8dVXX+Ghhx7C7t278eijj+ZybkWB/ITLl87GozMNnqpAE2LEIYWDKrvPZpHXkN04ctYKwwQaoM5gRDAEzNYLi+G0WSRNYzPRdgi1gHxB03u+gMR8twghsEw0QMnd4POdBg+IPEAqGqC2zhCYg7bSbdwDxOjuITDd3+5f//oXbr31VkybNo1aYGSR5BBYfj1AekXQlAJPMCo9drQHwqopuOJzJnceoMIXQmTe2oYDXvhDUdgsHA6pchdsPlpwHAePwyrUd8lUBA3EvMMNJhZAM4TCgfG1Lh09o2IWWAFF0Km0eOzxUqfNkIdKvs6TCDrOmjVr0N7ejtGjR2PMmDF47LHH0NTUlMu5FQVyz0re0+BTdIRvNphKSXR/qoXdp7L7nT1e5bEnqiWHc1QHqJCVoOPX6qbdsQKwA3qUFORmqBex0WO0Do4YcTXwruABkt/U0/EAKYugC3cOsmOgJoJO13MvDw+WdHMNkO4jd+KJJ2LhwoXYs2cPbrzxRrzwwgvo27cvotEoVq1ahfb29lzOs9tij9deYLjzJoKOp8EH9YbAyANExEhVC6hZwQPk72bNUIGEAfTt3pgBZFYRMENsAGWysxf3jPrBpE1QxcgNoOpMQmBdRASdrueePEAp8Hg8uOaaa7BmzRp8/fXX+L//+z/8/ve/R8+ePXHeeeflYo7dHvFJl786QHqzwFgmAXmAiBipUnDFrVNylQZvhhBYosZR7EZoZi8IIDWAshMCC6GhMZZZNbjWnNonIHntSmctcyi1wihoHSBtEXTaHqAi0wBldOQOP/xwPPDAA9i5cyeef/75bM2p6BCHl/LRDBXQXweIhTPIA0QwUnmADvoSFXedOSqEKIigTaABYphVAM0oFYUzsiGC3n7QhzZ/GBwH1PUoyXh+uUK+dmUrBBYsaB2ghBeO55PLUaTrAZIbTKWUBZYaq9WKCy64AP/4xz+y8XZFR2UBPEBCGnwKDRCJoAk5Cfd78u6T53mR1zCXHiATGEAOuQFkbg+QuK1BZhqg2E2SGaF9K9ymrhIv1y9mUgcooBACK4wIOvYdwlFeMZElXe2mvKwFaYCInCM+SfNmAMUXQK9ODRCJoAkGuwEqZYF5gxGhQm5ViV1ULDDLhRDNoAGSfbZZawAxSrMWApPeJM3+vcXz5ThpN3W9KHaDF2pR5T8N3u2wCsa/UihaWLcN1m8Tr/MOm8XUov5s0L2/XRdB7AHKeyFEjRCYPxQRdu4UAiMYWiEwVpfEYbPAbbcmGobmKARW0CwwkRfF47CiZ5mzYHPRgydLITCX3SqpWG92z5c4rFPuskvqIelFsRt8AbPAAG0hdLqee7fdKnyf7i6ABsgAMgVSEXR+DkmpjjpA7MKKdYDu/hcDoQ8tEbS4/xDHcZJ2EdnEFCJo0WcPqvUY7jCebyRp8BmGNsRrlpkzwADpXNP1ZDNPSCTKCy1gCtkNHtAWQie0m8a+L8dxwm+USZi0q0AGkAkQZyXk2wPkD0XVezqxi8htN/3iTuQPrZ1ns2znyc7ncJSXhA8yJWACD5BYA2R2ATQgbWuQ6e5e7BEeZOIMMCB2I2dZXOl6ssXnGTuPC+2FFAuh5civw3TelzxARF4ohAhabN2r6YDEYlaCYIh3nvIMlGZRBhggbVWRTS9Q0AwaILvYADK3FwRIbHosXOYbLbEnxeweII7jhPMxUw8QkPD8FFIEDSQ2zkrtMMSlKIzCfqvungIPkAFkCgohgnbaLLDFY+FqOiASQBNKsEU1GI7CJytwKF94nTYLmPMwm5lgARP0AhMbEWY3AoBEGrzHYcvYo8uOr8NqQd9Kc7b/EMPmm242q7jfFzO+gwVuyMs2zgdlITCe5yWlKIzCfiMygIi8UAgNEMdxKVPhE7t58gARCUocCaGkPAzWLPMachwnZEsFspgJVuibD4AuJQQGEmnw2UhtZjfWuh4laYmK8w2bb7prGcdxScUQQwWsAwQkKlrLQ2CdoYhwfaTjvWe/kYc0QEQ+EJ+k+aynkaoYYgv1ASMUEAsl5ULoFgWvodAQNaseoLgI2gTd4IGYCNrssOs9Gzv76via1RUMPyAx30zWMuYFCoXNLYJm/7ZbubSMmGoPhcCIPNKn3IUfH1qDc47pk9eLiemA1Aygnc2dAIDe5a68zYnoGqgJoQ96k8WXLFsqmx3hmTepkBqgPhVujBlUjfNG9EW5y/ybhFEDKjG0ZynOH9Ev4/caN6wn+le7MWlU5u+VD846ug8GVJdg3LCeab+HPX4eByOx8zjRCqMwHjA1ETTTBFWWONIKdZ42rBf6V7tx5vBemU/S5BTcAHr88ccxcOBAuFwujBkzBuvWrVMdGwqFMHfuXAwZMgQulwsjRozAypUrk8bt2rULP/3pT9GjRw+43W4cffTR+Oyzz3L5NTLCYuHw7HVj8NiVx+b1cz0pUuG3sEaHJs/yIPKP+u4zOWzK6uVkUwQdKHANFiBWHmL5jSfhkStGFWwORqgscWDVjLG4bfzQjN/r2AFV+OCu03DW0X2yMLPcc96Ivnj/rnE4ql9F2u/hEBqixjxABa8DxETQMgNIyQtrhNF1sWN75pG9M5tgF6CgBtDy5csxY8YMzJkzB1988QVGjBiB+vp67N+/X3H8rFmz8NRTT+HRRx/Fpk2bcNNNN2HSpElYv369MKa5uRknn3wy7HY7/vWvf2HTpk2YP38+qqqq8vW1ugzMJS4XsjIa4gZQV3FzE/lDbfepGALLcjsMnudN0QuMKC6EjvCyLLBCi6BZuRIGaTf1U9DVY8GCBbj++usxdepUDB8+HE8++SRKSkrwzDPPKI5funQp7rnnHkycOBGDBw/GtGnTMHHiRMyfP18Y84c//AH9+/fHokWLcMIJJ2DQoEE488wzMWTIkHx9rS4DC4EpeYDa/SE0tgcAdA19A5FfjCy+2W6HIe7HVEgPEFFcyBuiCiEwk9UBIu2mfgq2egSDQXz++ecYP358YjIWC8aPH4+PP/5Y8TWBQAAul1SP4na7sWbNGuHf//jHP3DcccfhkksuQc+ePTFq1CgsXLhQcy6BQABtbW2Sv2JASwTNvD81pc4uoW8g8ktViX73e7bbYQRFBRXJA0TkCyELLCzLAitUHaD4NeYNJrK+gERYuprqt6WkYKtHU1MTIpEIevWSCq169eqFvXv3Kr6mvr4eCxYswObNmxGNRrFq1SqsWLECe/bsEcZs2bIFTzzxBIYOHYq33noL06ZNw6233oolS5aozmXevHmoqKgQ/vr375+dL2lytPqBMQOoK9Q3IfKP0u4zGI4K3kSl0g7ZMoDE6fSFTIMnigu7Ld4RnnmACpwFVu6yg1UgEF+HFALTT5daPR5++GEMHToUw4YNg8PhwPTp0zF16lRYLImvEY1Gceyxx+J3v/sdRo0ahRtuuAHXX389nnzySdX3nTlzJlpbW4W/HTt25OPrFBzBAFLQAG1pJP0PoY6SCLqlM7bwchxQ7k4u7pktDRBLgXfYLNSihcgbcg9QoVthWCyc0Nlech1SAVvdFMwAqqmpgdVqxb59+ySP79u3D717K6vPa2tr8dprr8Hr9WLbtm349ttvUVpaisGDBwtj+vTpg+HDh0ted8QRR2D79u2qc3E6nSgvL5f8FQMejTR4QQBN+h9CASUPEFt4K9zSjtvZ1gCRAJooBHahEKIsC6yAXkilchTkAdJPwY6cw+HA6NGjsXr1auGxaDSK1atX46STTtJ8rcvlQr9+/RAOh/HKK6/g/PPPF547+eST8b///U8y/rvvvkNdXV12v0A3QCsNnjLACC0SKbiJnWezQg0gIPtZYAET9AEjig+HWh0gW+G8kIInVtQPTO06JJIpaKnHGTNmYMqUKTjuuONwwgkn4KGHHoLX68XUqVMBAJMnT0a/fv0wb948AMDatWuxa9cujBw5Ert27cJ9992HaDSKu+66S3jPO+64Az/60Y/wu9/9DpdeeinWrVuHv/zlL/jLX/5SkO9oZtTS4Hmex5bGDgDAEPIAEQoIO0/xwqviemcaoEC2RNDkASIKQCIExiMa5RGOFlYEDSSEzpKNCIXAdFNQA+iyyy5DY2MjZs+ejb1792LkyJFYuXKlIIzevn27RN/j9/sxa9YsbNmyBaWlpZg4cSKWLl2KyspKYczxxx+PV199FTNnzsTcuXMxaNAgPPTQQ7jqqqvy/fVMj5oHqLE9AG8wAgsH9K8uKcTUCJPDDKD2QBihSBR2q0WUfpsvDxAZQET+ENcBCkUT4dxCpcEDonIUFAJLi4I3+5g+fTqmT5+u+Nx7770n+ffYsWOxadOmlO95zjnn4JxzzsnG9Lo1rCmiXAPEKkAfUlVCYQZCkXK3HRwH8HxM+1Nb5hR1oJYuvE5mAGWpFYZYBE0Q+UJohRGOStLOC6sBYj35YtdeOBJFuz8seY5Qh1aQIkatDhDpf4hUWEUZKGzxVcs+EeoAhUkETXRdxN3gmRAaKFwaPCD2AMWuvZbORCiswk0GUCpoBSliPA7lNHgygAg9VMkWX0F8KSvA5sq6B4hE0ET+ccTFzjEDKHYOWi2cJOMx38izMdl/y1022KhGVkroFypiPGohsHgNIBJAE1pUyqpBM0OoUu4BcsRF0GEKgRFdF0EDJAqBFaoTPKNKVo9LEEBTFWhd0ApSxHhEWWDRaMKlu6UplgE2qIa6wBPqqO0+VUXQWfIAUQiMKARCN/gIL1SBLnQlcrkImnlhSQCtD1pBihimAQIAbzDmBQpHoth+wAeAiiAS2sirQSeyT6QeIKegAcpyCMxOyxeRP8QiaKEIYoGNcFaPi+nvqAq0MWgFKWKcNosQv2a1gHY2dyIc5eG0WdCn3KX1cqLIkVehTSy++fEAFXr3TRQXYhG0Wc5BsRc2GuWFa7GaPEC6oBWkiOE4DiXxdhisFpBYAG0poLiPMD9CCq43BJ7nhQwUuQGU7VYYJIImCgHz9ohF0IWsAQQkvK1RHmj3h0U6PDKA9EAGUJEjT4XfQhlghE7E+oM2fxiRuI4sSQQtGEDZ6gYfex8KgRH5hAmeg+GoYIQXMgUeiG0C2Ca22RcU6fAoBKYHWkGKHKEjfCB2U2mIC6AHk/6HSEGiDH9i4S1xWAWPD4O1wsiaAWQSASpRXDjElaAjhW+DwWAe14O+IA4yETRlgemi8EePKCgeuQeokXmAKAOM0EYsgj6o0YBR3AqD5/mk540SCJEImsg/dnEILGyOEBggFkIHSQRtkMIfPaKgeOLuU5YFRkUQCb2IBZgtKjWAgEQWWJSHkD6cCaQBIgqBpA6Q4IUsvE4y0Zg4JIigqRO8PsgAKnLEDVF9wTD2tPoBAIPJACJSkDCA9HmAgOwIoYUMHBPsvoniwSl4gHjTpMEDUi2eWjFSQpnCHz2ioDARtC8QwdamWP2fyhI7VRIlUsIW2XCUx87mTsljYuxWDiyhMBs6IFYJmgohEvlE3A0+aBIRNCBuiBpSLUZKKFP4o0cUFHEaPAt/kfeH0IPLbhW8O0w8r7TwchyX1UywAHmAiAKgFAIzgwHEPEA7m30IxzMxyQDSR+GPHlFQxGnwDdQCgzAI230y41lNfOkSCaEzJUgaIKIAOBRE0GYwwuXXoNNmgdtB14YeCn/0iIIiZIEFI0IGGKXAE3phu0927qgVYMtmMUQKgRGFQFwHyIxp8OwaJO+PfmyphxDdGXEa/PaD8R5gFAIjdMJScNvjZRTYv+WwHWk22mGQCJooBE6RB8gszVCBhO6OXYMkgNZP4Y8eUVCENPhAGFsaWQiMDCBCH3KPj7oHKF4MMQsNUQPUDZ4oAHZrIgtMEEHbzJMGz6imBBbd0ApS5DAP0I5mH9r8sR3EwB5kABH6kGt+1Nzvggg6Cx4gqgNEFAJmAAVMJoKWX3MUAtNP4Y8eUVCYCPr7/THvT79KNwnoCN3Iu06rdaEWNEBZ8ABRCIwoBKYVQcvCzhQC00/hjx5RUJgHKJ49SeEvwhBJITAVDZCQBRYkETTRNXFYRQaQiTRApU4bbJZEKI48QPop/NEjCkqJzNtDBhBhBPHu02bhUOZUzqvIZho8C4G5qBcYkUfMWgeI4zjJRoQ8QPop/NEjCkqp7IZFBhBhBPnCy3HKolB3FjvCCyEwK4VqifzBwl3hKC805DVDCAyQavHIA6Qfcxw9omB45AYQ1QAiDFAlMYDUF15XDipBUzd4Ip/YRY1PfXExvxk8QID0OlQrRUEkY46jRxQMj1O6ix5CVaAJA0h3nuoLb7ZaYYQjUUSi5ilCRxQPYmPHG4xlzJqhGzwgDXtpbUQIKbSCFDluu1VoVGm3cuhX5S7shIguRaVOD5AzSxogpr2IvSctX0T+EBvcHfGig+YJgTkU/5/QxhxHjygYHMfB44iFwep6eGC1mGNHQ3QNyl024ZzR5wHKLAssIHo9eYCIfGKxcEK2lS9grhCYOPtS6zokpJjj6BEFhemASABNGIXjOFS6Ywuu1s6TZWxlywNktXCwmeTmQxQPzONjVg+QhQPKXWQA6cUcR48oKCVxHdBgMoCINGD6A60QGPMABVIYQJEor6kTYh4gqgFEFALm8WEaILN4gJjXp8Jth4W8+Loxx9EjCkopeYCIDGC9hzRDYA59GqCfPr0WpzzwLrzxHbYcKoJIFBLBAGIeINMYQA7Jfwl9UDd4Auce0xcd/jBOPbxnoadCdEHOHdEXje0B/GhIjeoY1rdLSwMUjfJYt/UgIlEeWw94cWTfiqQxARO1ICCKj0RH+HgmoknOw+MGVuPQnqU4b0TfQk+lS0EGEIHrfzIY1/9kcKGnQXRRJp80EJNPGqg5RvAAaTRDbfeHhRT3Zm9IcQw1QiUKiV2W9m6WEFi1x4G3Z4wt9DS6HOY4egRBdGtcttSVoJt9QcX/F8NCYGbZeRPFhdzgkRtERNeCVhGCIHIO8wDpNYBaVAygYJhE0EThkBveZIh3bejoEQSRc/Q0Q23xJcJezb5UITBauoj8I/cAmUUETaQHHT2CIHKOnkKI+kJgJIImCofc4DGLBohIDzp6BEHkHKeoECLP84pjxF6fFhUPUJBE0EQBoRBY94KOHkEQOYd5gICEF0dOiwERNIXAiEJg1iwwIj3o6BEEkXNcIgNITQgtDYFpe4Bo500UAtIAdS/o6BEEkXPsVovQSFJNByQNgWlrgCgERhQCCoF1L+joEQSRF1JlgklCYF4VA4j1ArPT0kXkn2QRNNUB6srQKkIQRF4QDCCVatDi6s9t/jDCkWRPUTASL4RIoQeiAIg9PhYOsNF52KUxxdF7/PHHMXDgQLhcLowZMwbr1q1THRsKhTB37lwMGTIELpcLI0aMwMqVKyVj7rvvPnAcJ/kbNmxYrr8GQRAauB3xatDh1B4gAGjtTNYBkQeIKCRiDRAJoLs+BT+Cy5cvx4wZMzBnzhx88cUXGDFiBOrr67F//37F8bNmzcJTTz2FRx99FJs2bcJNN92ESZMmYf369ZJxRx55JPbs2SP8rVmzJh9fhyAIFVysIaqaB0gmfFbKBBM0QHTzIQqA2OghL2TXp+BHcMGCBbj++usxdepUDB8+HE8++SRKSkrwzDPPKI5funQp7rnnHkycOBGDBw/GtGnTMHHiRMyfP18yzmazoXfv3sJfTY16p2qCIHKP0A5DwQPkD0UEbVAPjwOAciaYUAfITiJoIv+IQ2AkgO76FPQIBoNBfP755xg/frzwmMViwfjx4/Hxxx8rviYQCMDlckkec7vdSR6ezZs3o2/fvhg8eDCuuuoqbN++PftfgCAI3TAPUGcwWdvDvD02C4dDqtyxxxSE0FQHiCgkDpHomUJgXZ+CHsGmpiZEIhH06tVL8nivXr2wd+9exdfU19djwYIF2Lx5M6LRKFatWoUVK1Zgz549wpgxY8Zg8eLFWLlyJZ544gk0NDTglFNOQXt7u+J7BgIBtLW1Sf4IgsguLo2GqEwAXVliR1XcA6RUDZpaYRCFRKIBslEGWFeny60iDz/8MIYOHYphw4bB4XBg+vTpmDp1KiyWxFc566yzcMkll+CYY45BfX093nzzTbS0tODFF19UfM958+ahoqJC+Ovfv3++vg5BFA0uW6IdhhwmgK4scaCqhIXAkj1A1A2eKCSSEBh5gLo8BT2CNTU1sFqt2Ldvn+Txffv2oXfv3oqvqa2txWuvvQav14tt27bh22+/RWlpKQYPHqz6OZWVlTjssMPw/fffKz4/c+ZMtLa2Cn87duxI/0sRBKGIW8sDFPf2VJXYUVlilzwmhgohEoWEssC6FwU9gg6HA6NHj8bq1auFx6LRKFavXo2TTjpJ87Uulwv9+vVDOBzGK6+8gvPPP191bEdHB3744Qf06dNH8Xmn04ny8nLJH0EQ2UXIAlM0gJI9QErVoKkVBlFI7CSC7lYU/AjOmDEDCxcuxJIlS/DNN99g2rRp8Hq9mDp1KgBg8uTJmDlzpjB+7dq1WLFiBbZs2YIPPvgAEyZMQDQaxV133SWMufPOO/Gf//wHW7duxUcffYRJkybBarXiiiuuyPv3IwgiRsIDlCyCZsZOVYkdVYIHiETQhLlwUhp8t8JW6AlcdtllaGxsxOzZs7F3716MHDkSK1euFITR27dvl+h7/H4/Zs2ahS1btqC0tBQTJ07E0qVLUVlZKYzZuXMnrrjiChw4cAC1tbX48Y9/jE8++QS1tbX5/noEQcRhxQuVNECJEJgDlSXqafAUAiMKiVj4TCGwrk/BDSAAmD59OqZPn6743HvvvSf599ixY7Fp0ybN93vhhReyNTWCILKEW6MXGPP2VHkcqPZQCIwwJw5rwvCmc7DrQ0eQIIi8wAwgJQ1Qi2ERNC1dRP6xUx2gbgUdQYIg8oJLwwBSE0HzPC8ZxzRAtPsmCoFUBE11gLo6tIoQBJEXEh4gJRF0QgPEDKBQhEdHICwZRx4gopCQCLp7QUeQIIi8IIigFZqhNouywNwOq2DgyKtBB6gXGFFAxB4gCoF1fegIEgSRFwQPkKwZaiTKo7WTtcKIeX+UqkHzPJ8QQdPNhygA0lYYdA52degIEgSRF5gGSO4Bau0MgUl9mABaSQgdjCRCZ8ybRBD5xEEhsG4FHUGCIPICK4TIwlgM5uUpc9qEHbZSNWjx60gDRBQCsfCZhPhdHzqCBEHkBdYKQ+4BEhqheuzCY1Xx/2/2JgygoMgAot03UQikvcAoC6yrQ6sIQRB5we2ILTdyDVCzN5EBxlCqBh0QFUHkOLr5EPlH2g2ehPhdHTKACILIC04VD5C4BhCD9QOThMBC1AeMKCxSETQZ4V0dWkkIgsgLYg1QNJoocCiuAs2oUvAAMRE0GUBEoZB6gOg87OrQESQIIi+4RLV7xILmRA0gpRCY2ANEjVCJwiLJAiNDvMtDR5AgiLzgEt0wxA1RxZ3gGdUeFgJT1gARRCGQiqDpPOzq0BEkCCIv2KwWYQct7gfWInSCT4TAlDxAQWqDQRQYq4WD1RLT/lAIrOtDR5AgiLwhtMOQeICURNBxA8grrgNEImii8LD0d6oE3fWhI0gQRN5wK3SEVxZBx/7fG4wInh8KgRFmgIW+HFQHqMtDKwlBEHnDpWAAKYmgy112xCMNQogsEQIjETRROJgHkgzxrg8dQYIg8kbCAxQzZnieFwohVoo8QBYLhwq3tB8YhcAIM8A8QCSC7vrQESQIIm+4mAYoXgzRF4wI9X3EHiDxv5tlHiDaeROFhAyg7gMdQYIg8oYQAot7c5hx47BaUOKQhrYqZdWgA5QFRpiA80b0xbDeZTiyb3mhp0JkiK3QEyAIonhgBhDzADEBdGWJPam/l7waNImgCTNwZ/3huLP+8EJPg8gCtJIQBJE35FlgSgJohrwWUIBE0ARBZBEygAiCyBtMA8RE0M2+ZAE0I9EQlUTQBEFkH1pJCILIG6whKiuE2KLhAarySIshsl5gFAIjCCIbkAaIIIi8wcJXQggsngLPjB0xcg1Qohu8uUJgkUgEoVAo9UCCIDLGbrfDas3OGkAGEEEQeUPuAUpogNRDYIIGiHWDt5vDA8TzPPbu3YuWlpZCT4UgiorKykr07t07KXHCKGQAEQSRN+SFELVCYMki6JjRZJYmlMz46dmzJ0pKSjJejAmC0Ibnefh8Puzfvx8A0KdPn4zejwwggiDyRkIEzTxAGiJoj1QELbTCMIEHKBKJCMZPjx49Cj0dgiga3G43AGD//v3o2bNnRuGwwq8kBEEUDfI0eE0RdPyxFl8Q0ShvqjR4pvkpKSkp8EwIovhg112m2jsygAiCyBtOu1QDdJAZQJ5kDxDzCkV5oN0fNmUrDAp7EUT+ydZ1Z56VhCCIbk+SB0hohJrsAXLarEJ7jGZfkOoAEUQXZvHixaisrCz0NCTQSkIQRN4QWmGEoghFomgPhAEoh8DEj8cMIPN5gLoaHMdp/t13330Zvfdrr72me/yNN94Iq9WKl156Ke3PJIxx9dVXS453jx49MGHCBHz11VeG3ue+++7DyJEjczPJPEIrCUEQeUPwAAUjgriZ44AKd3IIDBA3RA0lRNBkAKXNnj17hL+HHnoI5eXlksfuvPPOvMzD5/PhhRdewF133YVnnnkmL5+pRTAYLPQUsorW95kwYYJwvFevXg2bzYZzzjknj7MzD7SSEASRN4QssHBEEECXu+ywWpRj+koeIDOIoLsqvXv3Fv4qKirAcZzksRdeeAFHHHEEXC4Xhg0bhj//+c/Ca4PBIKZPn44+ffrA5XKhrq4O8+bNAwAMHDgQADBp0iRwHCf8W42XXnoJw4cPxy9/+Uu8//772LFjh+T5QCCAu+++G/3794fT6cShhx6Kv/71r8Lz//3vf3HOOeegvLwcZWVlOOWUU/DDDz8AAE499VTcfvvtkve74IILcPXVVwv/HjhwIH79619j8uTJKC8vxw033AAAuPvuu3HYYYehpKQEgwcPxq9+9askoe0///lPHH/88XC5XKipqcGkSZMAAHPnzsVRRx2V9F1HjhyJX/3qV4q/w3vvvQeO4/DGG2/gmGOOgcvlwoknnoiNGzdKxq1ZswannHIK3G43+vfvj1tvvRVerzfl91HC6XQKx3vkyJH45S9/iR07dqCxsVEYo/U7LF68GPfffz++/PJLwZO0ePFiAEBLSwtuvPFG9OrVCy6XC0cddRRef/11yee/9dZbOOKII1BaWioYY4WC0uAJgsgb4m7wLAVeqQgio1IohhgyvQaI53lB3J1v3HZrxsLQZcuWYfbs2XjssccwatQorF+/Htdffz08Hg+mTJmCRx55BP/4xz/w4osvYsCAAdixY4dguHz66afo2bMnFi1ahAkTJqRMTf7rX/+Kn/70p6ioqMBZZ52FxYsXS4yEyZMn4+OPP8YjjzyCESNGoKGhAU1NTQCAXbt24Sc/+QlOPfVUvPPOOygvL8eHH36IcDhs6Ps++OCDmD17NubMmSM8VlZWhsWLF6Nv3774+uuvcf3116OsrAx33XUXAOCNN97ApEmTcO+99+Jvf/sbgsEg3nzzTQDANddcg/vvvx+ffvopjj/+eADA+vXr8dVXX2HFihWac/nFL36Bhx9+GL1798Y999yDc889F9999x3sdjt++OEHTJgwAb/5zW/wzDPPoLGxEdOnT8f06dOxaNEize+Tio6ODjz77LM49NBDJeUctH6Hyy67DBs3bsTKlSvx9ttvAwAqKioQjUZx1llnob29Hc8++yyGDBmCTZs2Sc4Fn8+HBx98EEuXLoXFYsFPf/pT3HnnnVi2bJnuOWcTMoAIgsgbLpEImhU4VBJAM8Sp8GYPgXWGIhg++62CfPamufUocWS2nM+ZMwfz58/HhRdeCAAYNGgQNm3ahKeeegpTpkzB9u3bMXToUPz4xz8Gx3Goq6sTXltbWwsgUaFXi82bN+OTTz4RjIKf/vSnmDFjBmbNmgWO4/Ddd9/hxRdfxKpVqzB+/HgAwODBg4XXP/7446ioqMALL7wAuz1mIB922GGGv+9pp52G//u//5M8NmvWLOH/Bw4ciDvvvFMI1QHAb3/7W1x++eW4//77hXEjRowAABxyyCGor6/HokWLBANo0aJFGDt2rGT+SsyZMwdnnHEGAGDJkiU45JBD8Oqrr+LSSy/FvHnzcNVVVwleraFDh+KRRx7B2LFj8cQTT8Dlcql+HyVef/11lJaWAgC8Xi/69OmD119/HRZL4rrS+h3cbjdKS0ths9kkx/rf//431q1bh2+++UY4HvLvHQqF8OSTT2LIkCEAgOnTp2Pu3Lkp55wrzLmSEATRLWGtMPyhqBACq1boA8YQGqJSCCyneL1e/PDDD7j22mtRWloq/P3mN78RQktXX301NmzYgMMPPxy33nor/v3vf6f1Wc888wzq6+tRU1MDAJg4cSJaW1vxzjvvAAA2bNgAq9WKsWPHKr5+w4YNOOWUUwTjJ12OO+64pMeWL1+Ok08+Gb1790ZpaSlmzZqF7du3Sz779NNPV33P66+/Hs8//zz8fj+CwSCee+45XHPNNSnnctJJJwn/X11djcMPPxzffPMNAODLL7/E4sWLJcelvr4e0WgUDQ0Nmt9HiXHjxmHDhg3YsGED1q1bh/r6epx11lnYtm2b7t9BiQ0bNuCQQw7RNEZLSkoE4weIVXJmVZ0LAXmACILIG6649yYYieKAl3mA1G9kQj8wb8iUdYDEuO1WbJpbX7DPzoSOjg4AwMKFCzFmzBjJcyyEceyxx6KhoQH/+te/8Pbbb+PSSy/F+PHj8fLLL+v+nEgkgiVLlmDv3r2w2WySx5955hmcfvrpQqVfNVI9b7FYwPO85DGlgnkej0fy748//hhXXXUV7r//ftTX1wtepvnz5+v+7HPPPRdOpxOvvvoqHA4HQqEQLr74Ys3XpKKjowM33ngjbr311qTnBgwYoPp91PB4PDj00EOFfz/99NOoqKjAwoUL8Zvf/EbX76BEqt8GQJLRynFc0rHKJ2QAEQSRN5gHCAD2tvoBqKfAi59r6gggHI0tlGYNgXEcl3EYqlD06tULffv2xZYtW3DVVVepjisvL8dll12Gyy67DBdffDEmTJiAgwcPorq6Gna7HZGItgbqzTffRHt7O9avXy/RhmzcuBFTp05FS0sLjj76aESjUfznP/8RQmBijjnmGCxZsgShUEjRC1RbWysR1kYiEWzcuBHjxo3TnNtHH32Euro63HvvvcJjYq8I++zVq1dj6tSpiu9hs9kwZcoULFq0CA6HA5dffrkuw+CTTz4RjJnm5mZ89913OOKIIwDEDM9NmzZJjJZswnEcLBYLOjs7Aej7HRwOR9KxPuaYY7Bz50589913aYUkC0HXvFoJguiSuEThq90tzABKLYLe3x4QHjNDL7DuyP33349bb70VFRUVmDBhAgKBAD777DM0NzdjxowZWLBgAfr06YNRo0bBYrHgpZdeQu/evYXidgMHDsTq1atx8sknw+l0oqqqKukz/vrXv+Lss88WdDOM4cOH44477sCyZctwyy23YMqUKbjmmmsEEfS2bduwf/9+XHrppZg+fToeffRRXH755Zg5cyYqKirwySef4IQTTsDhhx+O0047DTNmzMAbb7yBIUOGYMGCBWhpaUn5/YcOHYrt27fjhRdewPHHH4833ngDr776qmTMnDlzcPrpp2PIkCG4/PLLEQ6H8eabb+Luu+8Wxlx33XWC8fLhhx/q+u3nzp2LHj16oFevXrj33ntRU1ODCy64AEAsI+vEE0/E9OnTcd1118Hj8WDTpk1YtWoVHnvsMV3vLyYQCGDv3r0AYsbWY489ho6ODpx77rm6f4eBAweioaFBCHuVlZVh7Nix+MlPfoKLLroICxYswKGHHopvv/0WHMdhwoQJhueZD2glIQgib1gsnBDC2tsW23HqEUEzbxFgnm7w3Y3rrrsOTz/9NBYtWoSjjz4aY8eOxeLFizFo0CAAscygBx54AMcddxyOP/54bN26FW+++aYgnp0/fz5WrVqF/v37Y9SoUUnvv2/fPrzxxhu46KKLkp6zWCyYNGmSkOr+xBNP4OKLL8bNN9+MYcOG4frrrxfSvnv06IF33nkHHR0dGDt2LEaPHo2FCxcK3qBrrrkGU6ZMweTJkwUBcirvDwCcd955uOOOOzB9+nSMHDkSH330UVL6+qmnnoqXXnoJ//jHPzBy5EicdtppWLdunWTM0KFD8aMf/QjDhg1LCieq8fvf/x633XYbRo8ejb179+Kf//wnHI7YuX/MMcfgP//5D7777juccsopGDVqFGbPno2+ffvqem85K1euRJ8+fdCnTx+MGTMGn376KV566SWceuqpun+Hiy66CBMmTMC4ceNQW1uL559/HgDwyiuv4Pjjj8cVV1yB4cOH46677krpFSwkHF/IAJxJaWtrQ0VFBVpbW1FeXl7o6RBEt2LE/f9Ga2cIPTwOHPAG8fiVx+LsY/oojt1+wIef/PFd4d9WC4cffjcxX1NVxe/3o6GhAYMGDRKycAgCiJVDGDp0KG6++WbMmDFDc+x7772HcePGobm52XRtIsyM1vVn5P5NITCCIPKKy25BaycEEbRmCEzWJJW8P4SZaWxsxAsvvIC9e/eq6oQI82CK1eTxxx/HwIED4XK5MGbMmCSXophQKIS5c+diyJAhcLlcGDFiBFauXKk6/ve//z04jkuqDEoQRGGQZyxphcDKnDbYRFWiSf9DmJmePXti7ty5+Mtf/qKogSLMRcE9QMuXL8eMGTPw5JNPYsyYMXjooYdQX1+P//3vf+jZs2fS+FmzZuHZZ5/FwoULMWzYMLz11luYNGkSPvroo6S486effoqnnnoKxxxzTL6+DkEQKXDJDKAqj7oHiOM4VJbY0dQR8xaZNQOMIAAYTuk+9dRTC5oGXuwUfDVZsGABrr/+ekydOhXDhw/Hk08+iZKSEtUGeUuXLsU999yDiRMnYvDgwZg2bRomTpyYVKOgo6MDV111FRYuXEiWOEGYiCQDSMMDBEg9RGatAUQQRNejoKtJMBjE559/Lqn1YLFYMH78eHz88ceKrwkEAkmiJ7fbjTVr1kgeu+WWW3D22Wcr1pFQes+2tjbJH0EQucElCmO57JYkg0iOWCNEVaAJgsgWBTWAmpqaEIlE0KtXL8njvXr1EuoUyKmvr8eCBQuwefNmRKNRrFq1CitWrJAUvnrhhRfwxRdfCJ2KUzFv3jxUVFQIf/3790//SxEEoYlYA5TK+wNIPUAUAiMIIlt0udXk4YcfxtChQzFs2DA4HA5Mnz4dU6dOFWpR7NixA7fddhuWLVumOz115syZaG1tFf5Yh2OCILKPy6ABVE0hMIIgckBBV5OamhpYrVbs27dP8vi+fftUOwrX1tbitddeg9frxbZt2/Dtt9+itLRU6Dr7+eefY//+/Tj22GNhs9lgs9nwn//8B4888ghsNptiUSan04ny8nLJH0EQuUHiAdIQQDPEqfDkASIIIlsUdDVxOBwYPXo0Vq9eLTwWjUaxevVqSXdcJVwuF/r164dwOIxXXnkF559/PgDg9NNPx9dffy10u92wYQOOO+44XHXVVUKXYYIgCodTZABppcAzqiQhMLp+CYLIDgXfTs2YMQMLFy7EkiVL8M0332DatGnwer1CEanJkydj5syZwvi1a9dixYoV2LJlCz744ANMmDAB0WgUd911F4BYufajjjpK8ufxeNCjRw8cddRRBfmOBEEkkGqAUnuAxGMoBFY8nHrqqZL6bQMHDsRDDz1UsPkQ3Y+CryaXXXYZHnzwQcyePRsjR47Ehg0bsHLlSkEYvX37donA2e/3Y9asWRg+fDgmTZqEfv36Yc2aNVRGnCC6CG5HYtkhEXRh2LFjB6655hr07dsXDocDdXV1uO2223DgwIH/b+/e46Iq8z+Af4bLDHcGURgIQRQMlIsmyeIlLDWwMtfaIgTFW5Zh4aUNdg0vpchq1mpJFzBwBUVpswxTVFBcEVAUspxJFFE0MRJBkZss8/394Y+zDgMGCkzMfN+v13m9OOd55pnnew7MfHnO5dF01x7alStXIBaL+R/eVm7cuIGQkBBYWFhAKpVizpw5uH379n1fU1JSgqlTp6Jfv36wsLDAyy+/rHLJyuHDhyESidpcTpw4odbe+fPnYW5urvZ9/fXXX8PHxwdSqRSmpqYYNmwYtm7d2iVx34/GH4QIAAsWLMCCBQvaLDt8+LDKur+/P+Ryeafab90GY0xz7p0RvrOnwHgE6OFduHABfn5+GDx4MLZv3w5nZ2ecOXMGf/3rX7F3717k5eWhT58+3fb+TU1NwsSl3SEpKQkvv/wyjhw5gvz8/A5PSNodmpubIRKJhJt0NCkkJATl5eU4cOAAmpqaMGvWLMybNw/btm1rs35tbS2efvppeHt7IysrCwAQHR2NyZMnIy8vD3p6ehg1apTKAEVLnczMTPj4+Khsb2pqQnBwMMaOHYtjx46plPXp0wdLly4Vbm5KT0/HrFmzYGNjg4CAgC7cC6o0f1QYYzrFWPzgp8D4GqCHFx4eDrFYjP3798Pf3x+Ojo6YNGkSDh48iF9++QVLly4FAPz9739vM3nw9vbGe++9J6wnJCTA3d0dRkZGcHNzQ1xcnFB28eJFiEQi7NixA/7+/jAyMkJKSgoqKysRHByMRx55BCYmJvD09BRmFH8YRITExERMnz4d06ZNE2aXv1dOTg7GjRsHExMTWFlZISAgAFVVVQDuXoO6du1auLi4QCKRwNHREatXrwbwv9GO6upqoa2ioiKIRCJcvHgRwN3kSyqVYvfu3RgyZAgkEgnKyspw4sQJTJw4EX379oWlpSX8/f1x6tQplX5VV1fjtddeg62tLYyMjODh4YH09HTU1tbCwsICX331lUr9b775Bqampqipqfnd/aJQKLBv3z4kJCTA19cXY8aMwccff4zU1FRcvXq1zdfk5OTg4sWLSEpKgqenJzw9PbFlyxYUFBQICZFYLIZMJhMWa2trfPvtt5g1axZEIpFKe++++y7c3Nzw8ssvq73XuHHjMHXqVLi7u2PQoEGIiIiAl5eX2vP9uhonQIyxHiXR9ucA1da2vzQ0dLxufX3H6nbCjRs3kJGRgTfeeAPGxsYqZTKZDCEhIdixYweICCEhITh+/DhKSkqEOmfOnMHp06cxbdo0AEBKSgqWLVuG1atXQ6FQICYmBtHR0diyZYtK21FRUYiIiIBCoUBAQAAaGhowYsQI7NmzBz/99BPmzZuH6dOn33ceyI44dOgQ6urqMGHCBISGhiI1NRW19+yjoqIijB8/HkOGDEFubi6OHj2KyZMnC3cH/+1vf0NsbCyio6Mhl8uxbds2tefU/Z66ujr84x//QEJCAs6cOQMbGxvU1NQgLCwMR48eRV5eHlxdXfHMM88IyYtSqcSkSZOQk5OD5ORkyOVyxMbGQl9fH6ampnjllVeQmJio8j6JiYn4y1/+AnNzc4wbNw4zZ85st0+5ubmQSqUqozITJkyAnp4e8vPz23xNY2MjRCIRJBKJsM3IyAh6enrtJia7d+9GZWWl2kSwWVlZSEtLw6ZNm+6774C7SWxmZibOnj2LJ5544nfrP4w/xCkwxpjuMFa5C6wDt8Gb9LLb4M3M2i975hlgz57/rdvYAHV1bdf19wfuPX0/YABw/bp6vU7MJXXu3DkQEdzd3dssd3d3R1VVFX777TcMHToU3t7e2LZtG6KjowHcTXh8fX3h4uICAFi+fDnWr1+PF154AQDg7OwMuVyOzz//HGFhYUK7CxcuFOq0ePvtt4Wf33zzTWRkZGDnzp0YOXJkh+NpbfPmzXjllVegr68PDw8PDBw4EGlpaUJysHbtWvj4+KiMUg0dOhQAUFNTgw0bNuCTTz4R+j5o0CCMGTOmU31oampCXFwcvL29hW1PPfWUSp0vvvgCUqkU2dnZeO6553Dw4EEcP34cCoUCgwcPBgDh0S4AMHfuXOF0k52dHSoqKvD999/j4MGDAABHR0fY2dm126dr166pza1pYGCAPn36tPvQ4T/96U8wNTVFZGQkYmJiQESIiopCc3Oz2mmvFps3b0ZAQAAcHByEbZWVlZg5cyaSk5Pv+4iZmzdv4pFHHkFjYyP09fURFxeHiRMntlu/K/SCTxPGmDa5dyqMjowAGerrwVxy93+1XpEA9QIdnYAzJCREuEaEiLB9+3aEhIQAuHuNSElJCebMmQMzMzNhWbVqlcqoEQC160Gam5vx/vvvw9PTE3369IGZmRkyMjJQVlb2wDFVV1fj66+/RmhoqLAtNDRU5TRYywhQWxQKBRobG9st7yixWKw2Afevv/6KV199Fa6urrC0tISFhQVu374txFtUVAQHBwch+Wlt5MiRGDp0qDCylpycDCcnJ2GE5F//+leHZz7oqH79+iEtLQ3fffcdzMzMYGlpierqajz22GNtXtN05coVZGRkYM6cOSrbX331VUybNu13R3PMzc1RVFSEEydOYPXq1Vi8eHG3X7/LI0CMsR7V2akwgLsPQ6xp/K/K6bM/rPvdWdP6OWQVFe3Xbf0l8//XmTwMFxcXiEQiKBQKTJ06Va1coVDAysoK/fr1AwAEBwcjMjISp06dQn19PS5fvoygoCAAEO4gio+PV7tWqPXz1kxNTVXW161bhw0bNuCf//wnPD09YWpqioULF+LOnTsPHNu2bdvQ0NCg0hciglKpRHFxMQYPHqx22u9e9ysDIHzp35s8NjU1tdlO6+tfwsLCUFlZiQ0bNsDJyQkSiQR+fn5CvL/33sDdUaBNmzYhKioKiYmJbV5n0x6ZTIaKVr9r//3vf3Hjxo12HzoMAE8//TRKSkpw/fp1GBgYQCqVQiaTqYxOtUhMTIS1tTWef/55le1ZWVnYvXs3PvjgAwD/OyYGBgb44osvMHv2bAB392/LyOKwYcOgUCiwZs0ajBs3rkMxPgj+d4ox1qNapsLQEwHmRh37H6wlURLr94KPLFPT9pfW0/Pcr27rL8X26nWCtbU1Jk6ciLi4ONS3usbo2rVrSElJQVBQkPDF6uDgAH9/f6SkpCAlJQUTJ04UTqXY2trC3t4eFy5cgIuLi8ri7Ox8337k5ORgypQpCA0Nhbe3NwYOHIji4uJOxdLa5s2bsWTJEpWH4P7www8YO3YsvvzySwCAl5eXyoN37+Xq6gpjY+N2y1uSwntP/xQVFXWobzk5OXjrrbfwzDPPYOjQoZBIJLh+z+lMLy8vXLly5b77IDQ0FJcuXcLGjRshl8tVTjH+Hj8/P1RXV+PkyZPCtqysLCiVyg7dJde3b19IpVJkZWWhoqJCLclpufh8xowZanf45ebmqhyT9957TxjtaSsJb6FUKtHY2NjhGB8IMTU3b94kAHTz5k1Nd4UxrVNw8QY5RabT8Pf2d/g1Mzbnk1NkOiXllHZfxzqhvr6e5HI51dfXa7ornVZcXEx9+/alsWPHUnZ2NpWVldHevXvJw8ODXF1dqbKyUqV+fHw82dvbU9++fWnr1q1qZcbGxrRhwwY6e/YsnT59mr788ktav349ERGVlpYSACosLFR53aJFi6h///6Uk5NDcrmc5s6dSxYWFjRlyhShjr+/P0VERAjrTk5O9NFHH7UZU2FhIQEghUKhVhYXF0cymYyampro7NmzJBaLaf78+fTDDz+QQqGguLg4+u2334iIaMWKFWRlZUVbtmyh8+fPU25uLiUkJBAR0Z07d6h///700ksvUXFxMaWnp9Ojjz5KAKi0tJSIiBITE8nS0lKtD8OHD6eJEyeSXC6nvLw8Gjt2LBkbG6vEM27cOPLw8KD9+/fThQsX6Pvvv6e9e/eqtDNt2jQSi8UUGBiosn369OkUFRXV5r5pERgYSMOHD6f8/Hw6evQoubq6UnBwsFB+5coVevTRRyk/P1/Y9uWXX1Jubi6dP3+etm7dSn369KHFixertX3w4MF2939rbe2jmJgY2r9/P5WUlJBcLqcPPviADAwMKD4+vs027vf315nvb06A2sAJEGPdp7axiSb98witSj/T4dfsPFFG/muzqPjarW7sWcf15gSIiOjixYsUFhZGtra2ZGhoSP3796c333yTrl+/rla3qqqKJBIJmZiYUE1NjVp5SkoKDRs2jMRiMVlZWdETTzxBX3/9NRG1nwBVVlbSlClTyMzMjGxsbOjdd9+lGTNmPHACtGDBAhoyZEibZeXl5aSnp0fffvstEREdPnyYRo0aRRKJhKRSKQUEBFBVVRURETU3N9OqVavIycmJDA0NydHRkWJiYoS2jh49Sp6enmRkZERjx46ltLS0DiVAp06dIh8fHzIyMiJXV1dKS0tTi6eyspJmzZpF1tbWZGRkRB4eHpSenq7STmZmJgGgnTt3qmz39/ensLCwNuO/t/3g4GAyMzMjCwsLmjVrlsrxbDlWhw4dErZFRkYKvyOurq60fv16UiqVam0HBwfTqFGj7vv+LdraR0uXLiUXFxcyMjIiKysr8vPzo9TU1Hbb6KoESETUiVsIdMStW7dgaWmJmzdv8sSojDE1DQ0NKC0thbOzM4xan9ZirJts3boVixYtwtWrVyEWd+z6OW10v7+/znx/80XQjDHG2B9YXV0dysvLERsbi9dee02nk5+u1AuuKGSMMcZ019q1a+Hm5gaZTKYyOTh7OJwAMcYYY39gK1asQFNTEzIzM2F2vwdtsk7hBIgxxhhjOocTIMYYY4zpHE6AGGPsAfFNtIz1vK76u+MEiDHGOqnlabd17U1kyhjrNi1/d62fOt1ZfBs8Y4x1kr6+PqRSqTC/komJSYfnZWKMPRgiQl1dHSoqKiCVStXmnOssToAYY+wBtEwi2XqSScZY92qZlPVhcQLEGGMPQCQSwc7ODjY2Nm3OCs4Y63qGhoYPPfLTghMgxhh7CPr6+l32gcwY6zl8ETRjjDHGdA4nQIwxxhjTOZwAMcYYY0zn8DVAbWh5yNKtW7c03BPGGGOMdVTL93ZHHpbICVAbampqAAD9+/fXcE8YY4wx1lk1NTWwtLS8bx0R8bPc1SiVSly9ehXm5uZd/nCzW7duoX///rh8+TIsLCy6tO0/Ml2NG+DYdTF2XY0b0N3YdTVu4I8VOxGhpqYG9vb20NO7/1U+PALUBj09PTg4OHTre1hYWGj8F0UTdDVugGPXxdh1NW5Ad2PX1biBP07svzfy04IvgmaMMcaYzuEEiDHGGGM6hxOgHiaRSLB8+XJIJBJNd6VH6WrcAMeui7HratyA7sauq3EDvTd2vgiaMcYYYzqHR4AYY4wxpnM4AWKMMcaYzuEEiDHGGGM6hxMgxhhjjOkcToB60KZNmzBgwAAYGRnB19cXx48f13SXutyRI0cwefJk2NvbQyQS4ZtvvlEpJyIsW7YMdnZ2MDY2xoQJE3Du3DnNdLYLrVmzBo8//jjMzc1hY2ODP//5zzh79qxKnYaGBoSHh8Pa2hpmZmZ48cUX8euvv2qox13n008/hZeXl/AQND8/P+zdu1co19a4W4uNjYVIJMLChQuFbdoa+4oVKyASiVQWNzc3oVxb427xyy+/IDQ0FNbW1jA2NoanpycKCgqEcm38nBswYIDaMReJRAgPDwfQO485J0A9ZMeOHVi8eDGWL1+OU6dOwdvbGwEBAaioqNB017pUbW0tvL29sWnTpjbL165di40bN+Kzzz5Dfn4+TE1NERAQgIaGhh7uadfKzs5GeHg48vLycODAATQ1NeHpp59GbW2tUGfRokX47rvvkJaWhuzsbFy9ehUvvPCCBnvdNRwcHBAbG4uTJ0+ioKAATz31FKZMmYIzZ84A0N6473XixAl8/vnn8PLyUtmuzbEPHToU5eXlwnL06FGhTJvjrqqqwujRo2FoaIi9e/dCLpdj/fr1sLKyEupo4+fciRMnVI73gQMHAAAvvfQSgF56zIn1iJEjR1J4eLiw3tzcTPb29rRmzRoN9qp7AaBdu3YJ60qlkmQyGa1bt07YVl1dTRKJhLZv366BHnafiooKAkDZ2dlEdDdOQ0NDSktLE+ooFAoCQLm5uZrqZrexsrKihIQEnYi7pqaGXF1d6cCBA+Tv708RERFEpN3HfPny5eTt7d1mmTbHTUQUGRlJY8aMabdcVz7nIiIiaNCgQaRUKnvtMecRoB5w584dnDx5EhMmTBC26enpYcKECcjNzdVgz3pWaWkprl27prIfLC0t4evrq3X74ebNmwCAPn36AABOnjyJpqYmldjd3Nzg6OioVbE3NzcjNTUVtbW18PPz04m4w8PD8eyzz6rECGj/MT937hzs7e0xcOBAhISEoKysDID2x7179274+PjgpZdego2NDYYPH474+HihXBc+5+7cuYPk5GTMnj0bIpGo1x5zToB6wPXr19Hc3AxbW1uV7ba2trh27ZqGetXzWmLV9v2gVCqxcOFCjB49Gh4eHgDuxi4WiyGVSlXqakvsP/74I8zMzCCRSPD6669j165dGDJkiNbHnZqailOnTmHNmjVqZdocu6+vL5KSkrBv3z58+umnKC0txdixY1FTU6PVcQPAhQsX8Omnn8LV1RUZGRmYP38+3nrrLWzZsgWAbnzOffPNN6iursbMmTMB9N7fdZ4NnrEuFh4ejp9++knlmght9+ijj6KoqAg3b97EV199hbCwMGRnZ2u6W93q8uXLiIiIwIEDB2BkZKTp7vSoSZMmCT97eXnB19cXTk5O2LlzJ4yNjTXYs+6nVCrh4+ODmJgYAMDw4cPx008/4bPPPkNYWJiGe9czNm/ejEmTJsHe3l7TXXkoPALUA/r27Qt9fX21K+J//fVXyGQyDfWq57XEqs37YcGCBUhPT8ehQ4fg4OAgbJfJZLhz5w6qq6tV6mtL7GKxGC4uLhgxYgTWrFkDb29vbNiwQavjPnnyJCoqKvDYY4/BwMAABgYGyM7OxsaNG2FgYABbW1utjb01qVSKwYMH4/z581p9zAHAzs4OQ4YMUdnm7u4unALU9s+5S5cu4eDBg5g7d66wrbcec06AeoBYLMaIESOQmZkpbFMqlcjMzISfn58Ge9aznJ2dIZPJVPbDrVu3kJ+f3+v3AxFhwYIF2LVrF7KysuDs7KxSPmLECBgaGqrEfvbsWZSVlfX62NuiVCrR2Nio1XGPHz8eP/74I4qKioTFx8cHISEhws/aGntrt2/fRklJCezs7LT6mAPA6NGj1R5xUVxcDCcnJwDa/TkHAImJibCxscGzzz4rbOu1x1zTV2HritTUVJJIJJSUlERyuZzmzZtHUqmUrl27pumudamamhoqLCykwsJCAkAffvghFRYW0qVLl4iIKDY2lqRSKX377bd0+vRpmjJlCjk7O1N9fb2Ge/5w5s+fT5aWlnT48GEqLy8Xlrq6OqHO66+/To6OjpSVlUUFBQXk5+dHfn5+Gux114iKiqLs7GwqLS2l06dPU1RUFIlEItq/fz8RaW/cbbn3LjAi7Y19yZIldPjwYSotLaWcnByaMGEC9e3blyoqKohIe+MmIjp+/DgZGBjQ6tWr6dy5c5SSkkImJiaUnJws1NHWz7nm5mZydHSkyMhItbLeeMw5AepBH3/8MTk6OpJYLKaRI0dSXl6eprvU5Q4dOkQA1JawsDAiunuLaHR0NNna2pJEIqHx48fT2bNnNdvpLtBWzAAoMTFRqFNfX09vvPEGWVlZkYmJCU2dOpXKy8s11+kuMnv2bHJyciKxWEz9+vWj8ePHC8kPkfbG3ZbWCZC2xh4UFER2dnYkFovpkUceoaCgIDp//rxQrq1xt/juu+/Iw8ODJBIJubm50RdffKFSrq2fcxkZGQSgzVh64zEXERFpZOiJMcYYY0xD+BogxhhjjOkcToAYY4wxpnM4AWKMMcaYzuEEiDHGGGM6hxMgxhhjjOkcToAYY4wxpnM4AWKMMcaYzuEEiDGmVZKSktRmpe4pK1aswLBhwzTy3oyxzuEEiDHW5WbOnAmRSCQs1tbWCAwMxOnTpzvVTk8lFBcvXoRIJEJRUVG3vxdj7I+BEyDGWLcIDAxEeXk5ysvLkZmZCQMDAzz33HOa7hZjjAHgBIgx1k0kEglkMhlkMhmGDRuGqKgoXL58Gb/99ptQJzIyEoMHD4aJiQkGDhyI6OhoNDU1Abh7KmvlypX44YcfhJGkpKQkAEB1dTVee+012NrawsjICB4eHkhPT1d5/4yMDLi7u8PMzExIxjrq8OHDEIlEyMzMhI+PD0xMTDBq1Ci1WcBjY2Nha2sLc3NzzJkzBw0NDWptJSQkwN3dHUZGRnBzc0NcXJxQNnv2bHh5eaGxsREAcOfOHQwfPhwzZszocF8ZYw+GEyDGWLe7ffs2kpOT4eLiAmtra2G7ubk5kpKSIJfLsWHDBsTHx+Ojjz4CAAQFBWHJkiUYOnSoMJIUFBQEpVKJSZMmIScnB8nJyZDL5YiNjYW+vr7Qbl1dHT744ANs3boVR44cQVlZGd5+++1O93vp0qVYv349CgoKYGBggNmzZwtlO3fuxIoVKxATE4OCggLY2dmpJDcAkJKSgmXLlmH16tVQKBSIiYlBdHQ0tmzZAgDYuHEjamtrERUVJbxfdXU1Pvnkk073lTHWSZqejZUxpn3CwsJIX1+fTE1NydTUlACQnZ0dnTx58r6vW7duHY0YMUJYX758OXl7e6vUycjIID09vXZn105MTCQAKrOTb9q0iWxtbdt939LSUgJAhYWFRER06NAhAkAHDx4U6uzZs4cAUH19PRER+fn50RtvvKHSjq+vr0p/Bw0aRNu2bVOp8/7775Ofn5+wfuzYMTI0NKTo6GgyMDCg//znP+32kzHWdXgEiDHWLZ588kkUFRWhqKgIx48fR0BAACZNmoRLly4JdXbs2IHRo0dDJpPBzMwM7777LsrKyu7bblFRERwcHDB48OB265iYmGDQoEHCup2dHSoqKjodg5eXl0obAIR2FAoFfH19Ver7+fkJP9fW1qKkpARz5syBmZmZsKxatQolJSUqr3n77bfx/vvvY8mSJRgzZkyn+8kY6zwDTXeAMaadTE1N4eLiIqwnJCTA0tIS8fHxWLVqFXJzcxESEoKVK1ciICAAlpaWSE1Nxfr16+/brrGx8e++t6Ghocq6SCQCEXU6hnvbEYlEAAClUtmh196+fRsAEB8fr5Yo3Xu6TqlUIicnB/r6+jh//nyn+8gYezA8AsQY6xEikQh6enqor68HABw7dgxOTk5YunQpfHx84OrqqjI6BABisRjNzc0q27y8vHDlyhUUFxf3WN/b4u7ujvz8fJVteXl5ws+2trawt7fHhQsX4OLiorI4OzsL9datW4eff/4Z2dnZ2LdvHxITE3ssBsZ0GY8AMca6RWNjI65duwYAqKqqwieffILbt29j8uTJAABXV1eUlZUhNTUVjz/+OPbs2YNdu3aptDFgwACUlpYKp73Mzc3h7++PJ554Ai+++CI+/PBDuLi44Oeff4ZIJEJgYGCPxRcREYGZM2fCx8cHo0ePRkpKCs6cOYOBAwcKdVauXIm33noLlpaWCAwMRGNjIwoKClBVVYXFixejsLAQy5Ytw1dffYXRo0fjww8/REREBPz9/VXaYYx1PR4BYox1i3379sHOzg52dnbw9fXFiRMnkJaWhnHjxgEAnn/+eSxatAgLFizAsGHDcOzYMURHR6u08eKLLyIwMBBPPvkk+vXrh+3btwMA/v3vf+Pxxx9HcHAwhgwZgnfeeUdtpKi7BQUFITo6Gu+88w5GjBiBS5cuYf78+Sp15s6di4SEBCQmJsLT0xP+/v5ISkqCs7MzGhoaEBoaipkzZwpJ4bx58/Dkk09i+vTpPR4PY7pGRA9yYpwxxhhjrBfjESDGGGOM6RxOgBhjjDGmczgBYowxxpjO4QSIMcYYYzqHEyDGGGOM6RxOgBhjjDGmczgBYowxxpjO4QSIMcYYYzqHEyDGGGOM6RxOgBhjjDGmczgBYowxxpjO4QSIMcYYYzrn/wC8MeFvdtec+gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 加载数据\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "height = y.shape[0]\n",
        "width = y.shape[1]\n",
        "\n",
        "# 假设已经应用了PCA和填充零\n",
        "X = applyPCA(X, numComponents=pca_components)\n",
        "X = padWithZeros(X, patch_size//2)\n",
        "\n",
        "# 逐像素预测类别\n",
        "outputs = np.zeros((height, width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        if int(y[i, j]) == 0:\n",
        "            continue\n",
        "        else:\n",
        "            image_patch = X[i:i + patch_size, j:j + patch_size, :]\n",
        "            image_patch = image_patch.reshape(1, image_patch.shape[0], image_patch.shape[1], image_patch.shape[2], 1)\n",
        "            X_test_image = torch.FloatTensor(image_patch.transpose(0, 4, 3, 1, 2)).to(device)\n",
        "            prediction = net(X_test_image)\n",
        "            prediction = np.argmax(prediction.detach().cpu().numpy(), axis=1)\n",
        "            outputs[i][j] = prediction + 1\n",
        "\n",
        "# 定义Indian Pines的类别标签\n",
        "target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn',\n",
        "                'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed',\n",
        "                'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',\n",
        "                'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',\n",
        "                'Stone-Steel-Towers']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoILaxWUmJVj",
        "outputId": "19f2192d-984d-4da1-e9e8-f58f66310f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-8aca184c64ab>:24: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  outputs[i][j] = prediction + 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import numpy as np\n",
        "from matplotlib.colors import Normalize\n",
        "\n",
        "# 设置颜色映射表和归一化\n",
        "cmap = plt.cm.Spectral\n",
        "norm = Normalize(vmin=1, vmax=len(target_names))  # 确保vmin和vmax是基于类别数来设定\n",
        "\n",
        "# 显示预测图像，使用 matplotlib 的 imshow 来绘制\n",
        "plt.imshow(outputs, cmap=cmap, norm=norm)\n",
        "plt.colorbar()  # 显示色条\n",
        "plt.title('Predicted Image')\n",
        "\n",
        "# 创建自定义图例\n",
        "patches = [mpatches.Patch(color=cmap(i / len(target_names)), label=label)\n",
        "           for i, label in enumerate(target_names,start=1)]\n",
        "\n",
        "# 显示图例\n",
        "plt.legend(handles=patches, bbox_to_anchor=(1.25, 0.95), loc='upper left', borderaxespad=0.)\n",
        "\n",
        "# 保存图像到本地文件\n",
        "plt.savefig('predicted_image_NoSE_NoSoftmax_NoDropoutControl.png', bbox_inches='tight')  # 保存为PNG文件，'tight' 会自动调整边距\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7hcHvAbxmQcf",
        "outputId": "d9a7678a-3c0f-4c5b-c491-4d24e57b4828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}